Nation-wide youth and covid data (Tessa)
Pear Tree Statistics Consulting
2024-09-30
jmv::descriptives(mydata, vars = vars( LBStotal, CASI_ADHD_CSum), missing = TRUE)##
## DESCRIPTIVES
##
## Descriptives
## ────────────────────────────────────────────────────
## LBStotal CASI_ADHD_CSum
## ────────────────────────────────────────────────────
## N 395 432
## Missing 145 108
## Mean 25.82278 11.10648
## Median 25 11.00000
## Standard deviation 6.537202 4.051038
## Minimum 10 0
## Maximum 46 24
## ────────────────────────────────────────────────────jmv::descriptives(mydata, vars = vars(COVIDrestrictionstotal, COVID_childimpact_T1, COVIDstressscale), missing = TRUE)##
## DESCRIPTIVES
##
## Descriptives
## ────────────────────────────────────────────────────────────────────────────────────────────
## COVIDrestrictionstotal COVID_childimpact_T1 COVIDstressscale
## ────────────────────────────────────────────────────────────────────────────────────────────
## N 392 398 398
## Missing 148 142 142
## Mean 59.63776 19.13819 31.19598
## Median 62.00000 19.00000 32.00000
## Standard deviation 27.34195 5.035241 8.921815
## Minimum 0 8 2
## Maximum 106 36 55
## ────────────────────────────────────────────────────────────────────────────────────────────jmv::descriptives(mydata, vars = vars(anxiety_T1, anxiety_T2, anxiety_T3, anxiety_T4))##
## DESCRIPTIVES
##
## Descriptives
## ──────────────────────────────────────────────────────────────────────────────
## anxiety_T1 anxiety_T2 anxiety_T3 anxiety_T4
## ──────────────────────────────────────────────────────────────────────────────
## N 394 147 163 110
## Missing 146 393 377 430
## Mean 1.256839 1.422902 1.297546 1.405051
## Median 1.333333 1.444444 1.333333 1.444444
## Standard deviation 0.5985536 0.4171743 0.4953677 0.5071884
## Minimum 0.000000 0.000000 0.000000 0.000000
## Maximum 3.000000 2.555556 2.666667 2.333333
## ──────────────────────────────────────────────────────────────────────────────jmv::descriptives(mydata, vars = vars(depression_T1, depression_T2, depression_T3, depression_T4))##
## DESCRIPTIVES
##
## Descriptives
## ──────────────────────────────────────────────────────────────────────────────────────────
## depression_T1 depression_T2 depression_T3 depression_T4
## ──────────────────────────────────────────────────────────────────────────────────────────
## N 394 147 163 110
## Missing 146 393 377 430
## Mean 1.192470 1.325964 1.239264 1.363636
## Median 1.250000 1.250000 1.250000 1.500000
## Standard deviation 0.6964080 0.5275807 0.6011497 0.6016380
## Minimum 0.000000 0.000000 0.000000 0.000000
## Maximum 3.000000 2.500000 3.000000 2.750000
## ──────────────────────────────────────────────────────────────────────────────────────────tabyl(mydata$income.f)## mydata$income.f n percent valid_percent
## less than 40k 55 0.1018519 0.1185345
## 40-65k 80 0.1481481 0.1724138
## 66-80k 68 0.1259259 0.1465517
## 80-106k 119 0.2203704 0.2564655
## over 106k 142 0.2629630 0.3060345
## <NA> 76 0.1407407 NAtabyl(mydata$education.f)## mydata$education.f n percent valid_percent
## less than high school 2 0.003703704 0.00422833
## high school or GED 25 0.046296296 0.05285412
## some college 45 0.083333333 0.09513742
## college 90 0.166666667 0.19027484
## bachelor degree 201 0.372222222 0.42494715
## some graduate work 36 0.066666667 0.07610994
## masters degree 57 0.105555556 0.12050740
## doctoral degree 17 0.031481481 0.03594080
## prefer not to answer 0 0.000000000 0.00000000
## <NA> 67 0.124074074 NAtabyl(mydata$child.age.f)## mydata$child.age.f n percent valid_percent
## 0-5 13 0.02407407 0.02748414
## 5-10 253 0.46851852 0.53488372
## 10-15 187 0.34629630 0.39534884
## 15-20 20 0.03703704 0.04228330
## <NA> 67 0.12407407 NAtabyl(mydata$school.modality.f)## mydata$school.modality.f n percent valid_percent
## in person 303 0.56111111 0.6460554
## alternating 113 0.20925926 0.2409382
## virtual 53 0.09814815 0.1130064
## <NA> 71 0.13148148 NAtabyl(mydata$child.race.f)## mydata$child.race.f n percent valid_percent
## Aboriginal/ First Nations/ Métis/ Inuit 80 0.148148148 0.177777778
## White/ Caucasian 303 0.561111111 0.673333333
## South Asian 1 0.001851852 0.002222222
## Black 20 0.037037037 0.044444444
## Filipino 1 0.001851852 0.002222222
## Latin American 16 0.029629630 0.035555556
## Arab 0 0.000000000 0.000000000
## Southeast Asian 0 0.000000000 0.000000000
## West Asian 0 0.000000000 0.000000000
## Korean 0 0.000000000 0.000000000
## Japanese 1 0.001851852 0.002222222
## Multi-race 4 0.007407407 0.008888889
## Prefer not to answer 22 0.040740741 0.048888889
## Other 2 0.003703704 0.004444444
## <NA> 90 0.166666667 NAtabyl(mydata$child.gender.f)## mydata$child.gender.f n percent valid_percent
## boy 270 0.5000000 0.5708245
## girl 203 0.3759259 0.4291755
## neither option applies 0 0.0000000 0.0000000
## <NA> 67 0.1240741 NAtabyl(mydata$DxTotal)## mydata$DxTotal n percent valid_percent
## 0 164 0.303703704 0.34672304
## 1 107 0.198148148 0.22621564
## 2 123 0.227777778 0.26004228
## 3 46 0.085185185 0.09725159
## 4 27 0.050000000 0.05708245
## 5 4 0.007407407 0.00845666
## 6 2 0.003703704 0.00422833
## NA 67 0.124074074 NAmydata %>%
dplyr::select(CASI_17,CASI_18,CASI_19,CASI_20,CASI_21,CASI_22,CASI_23,CASI_24,CASI_25) %>%
psych::alpha()##
## Reliability analysis
## Call: psych::alpha(x = .)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.86 0.86 0.86 0.41 6.4 0.0088 1.3 0.6 0.41
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.85 0.86 0.88
## Duhachek 0.85 0.86 0.88
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CASI_17 0.85 0.85 0.84 0.42 5.7 0.0097 0.0027 0.41
## CASI_18 0.85 0.85 0.84 0.41 5.6 0.0099 0.0027 0.41
## CASI_19 0.85 0.85 0.83 0.41 5.5 0.0101 0.0023 0.41
## CASI_20 0.85 0.85 0.84 0.41 5.5 0.0100 0.0027 0.41
## CASI_21 0.85 0.85 0.84 0.42 5.8 0.0096 0.0031 0.42
## CASI_22 0.85 0.85 0.84 0.42 5.8 0.0096 0.0024 0.41
## CASI_23 0.85 0.85 0.84 0.42 5.8 0.0096 0.0030 0.42
## CASI_24 0.85 0.85 0.84 0.42 5.7 0.0097 0.0028 0.42
## CASI_25 0.85 0.85 0.84 0.41 5.7 0.0098 0.0025 0.41
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CASI_17 392 0.69 0.69 0.64 0.59 1.3 0.87
## CASI_18 394 0.71 0.71 0.66 0.62 1.2 0.89
## CASI_19 393 0.73 0.73 0.69 0.64 1.3 0.88
## CASI_20 393 0.72 0.72 0.68 0.63 1.2 0.87
## CASI_21 393 0.67 0.67 0.61 0.56 1.3 0.88
## CASI_22 392 0.67 0.67 0.62 0.57 1.3 0.85
## CASI_23 389 0.66 0.67 0.61 0.56 1.3 0.82
## CASI_24 393 0.68 0.68 0.63 0.58 1.2 0.86
## CASI_25 393 0.69 0.69 0.64 0.60 1.3 0.86
##
## Non missing response frequency for each item
## 0 1 2 3 miss
## CASI_17 0.20 0.42 0.30 0.08 0.27
## CASI_18 0.24 0.43 0.25 0.08 0.27
## CASI_19 0.20 0.44 0.26 0.09 0.27
## CASI_20 0.20 0.44 0.27 0.08 0.27
## CASI_21 0.18 0.42 0.30 0.10 0.27
## CASI_22 0.18 0.45 0.29 0.09 0.27
## CASI_23 0.14 0.47 0.31 0.08 0.28
## CASI_24 0.22 0.44 0.27 0.07 0.27
## CASI_25 0.19 0.45 0.27 0.08 0.27#time 1 anxiety: .86
mydata %>%
dplyr::select(CASI_18,CASI_26,CASI_27,CASI_28) %>%
psych::alpha()##
## Reliability analysis
## Call: psych::alpha(x = .)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.79 0.79 0.74 0.49 3.8 0.015 1.2 0.7 0.49
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.76 0.79 0.82
## Duhachek 0.76 0.79 0.82
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CASI_18 0.71 0.72 0.63 0.46 2.5 0.021 0.00117 0.45
## CASI_26 0.73 0.73 0.64 0.47 2.7 0.020 0.00065 0.48
## CASI_27 0.76 0.76 0.68 0.52 3.2 0.018 0.00276 0.49
## CASI_28 0.75 0.75 0.67 0.50 3.0 0.019 0.00551 0.50
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CASI_18 394 0.81 0.81 0.73 0.65 1.2 0.89
## CASI_26 392 0.79 0.80 0.70 0.62 1.2 0.87
## CASI_27 393 0.76 0.76 0.62 0.56 1.2 0.91
## CASI_28 393 0.77 0.77 0.65 0.58 1.2 0.89
##
## Non missing response frequency for each item
## 0 1 2 3 miss
## CASI_18 0.24 0.43 0.25 0.08 0.27
## CASI_26 0.24 0.41 0.28 0.06 0.27
## CASI_27 0.24 0.42 0.24 0.09 0.27
## CASI_28 0.21 0.42 0.27 0.09 0.27#time 1 depression: .79
mydata %>%
dplyr::select(CASI_17_T3,CASI_18_T3,CASI_19_T3,CASI_20_T3,CASI_21_T3,CASI_22_T3,CASI_23_T3,CASI_24_T3,CASI_25_T3) %>%
psych::alpha()##
## Reliability analysis
## Call: psych::alpha(x = .)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.79 0.79 0.78 0.29 3.8 0.014 1.3 0.5 0.29
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.76 0.79 0.82
## Duhachek 0.76 0.79 0.82
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CASI_17_T3 0.78 0.78 0.77 0.30 3.4 0.015 0.0043 0.30
## CASI_18_T3 0.77 0.77 0.76 0.29 3.3 0.015 0.0052 0.30
## CASI_19_T3 0.76 0.76 0.75 0.29 3.2 0.016 0.0046 0.27
## CASI_20_T3 0.77 0.77 0.76 0.29 3.3 0.015 0.0049 0.29
## CASI_21_T3 0.78 0.78 0.77 0.30 3.4 0.015 0.0046 0.31
## CASI_22_T3 0.77 0.77 0.75 0.29 3.3 0.015 0.0032 0.28
## CASI_23_T3 0.77 0.77 0.76 0.30 3.4 0.015 0.0032 0.28
## CASI_24_T3 0.78 0.78 0.77 0.30 3.5 0.015 0.0050 0.32
## CASI_25_T3 0.76 0.76 0.75 0.29 3.2 0.015 0.0048 0.28
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CASI_17_T3 163 0.57 0.58 0.49 0.44 1.3 0.77
## CASI_18_T3 163 0.62 0.61 0.54 0.48 1.3 0.86
## CASI_19_T3 163 0.67 0.66 0.60 0.54 1.2 0.88
## CASI_20_T3 163 0.63 0.63 0.56 0.50 1.3 0.83
## CASI_21_T3 162 0.59 0.58 0.50 0.44 1.4 0.85
## CASI_22_T3 162 0.63 0.63 0.58 0.50 1.2 0.81
## CASI_23_T3 163 0.59 0.60 0.53 0.46 1.3 0.76
## CASI_24_T3 163 0.56 0.57 0.47 0.42 1.3 0.78
## CASI_25_T3 163 0.64 0.65 0.59 0.52 1.3 0.75
##
## Non missing response frequency for each item
## 0 1 2 3 miss
## CASI_17_T3 0.15 0.40 0.41 0.03 0.7
## CASI_18_T3 0.20 0.42 0.31 0.07 0.7
## CASI_19_T3 0.23 0.38 0.33 0.07 0.7
## CASI_20_T3 0.15 0.44 0.33 0.08 0.7
## CASI_21_T3 0.15 0.42 0.34 0.09 0.7
## CASI_22_T3 0.19 0.44 0.33 0.05 0.7
## CASI_23_T3 0.13 0.47 0.35 0.05 0.7
## CASI_24_T3 0.15 0.44 0.36 0.05 0.7
## CASI_25_T3 0.13 0.51 0.31 0.05 0.7#time 3 anxiety: .79
mydata %>%
dplyr::select(CASI_18_T3,CASI_26_T3,CASI_27_T3,CASI_28_T3) %>%
psych::alpha()##
## Reliability analysis
## Call: psych::alpha(x = .)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.69 0.69 0.65 0.36 2.2 0.022 1.2 0.6 0.33
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.64 0.69 0.73
## Duhachek 0.65 0.69 0.73
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CASI_18_T3 0.63 0.64 0.56 0.37 1.7 0.027 0.0225 0.32
## CASI_26_T3 0.53 0.53 0.43 0.27 1.1 0.035 0.0034 0.25
## CASI_27_T3 0.71 0.71 0.63 0.45 2.4 0.022 0.0101 0.47
## CASI_28_T3 0.61 0.61 0.53 0.34 1.6 0.029 0.0150 0.32
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CASI_18_T3 163 0.71 0.71 0.55 0.46 1.3 0.86
## CASI_26_T3 163 0.81 0.81 0.75 0.62 1.3 0.84
## CASI_27_T3 163 0.63 0.62 0.40 0.33 1.1 0.84
## CASI_28_T3 163 0.73 0.74 0.61 0.50 1.3 0.81
##
## Non missing response frequency for each item
## 0 1 2 3 miss
## CASI_18_T3 0.20 0.42 0.31 0.07 0.7
## CASI_26_T3 0.18 0.44 0.31 0.07 0.7
## CASI_27_T3 0.25 0.40 0.31 0.04 0.7
## CASI_28_T3 0.17 0.44 0.34 0.06 0.7#time 3 depression: .69
mydata %>%
dplyr::select(LBS_1, LBS_2, LBS_3, LBS_4, LBS_5, LBS_6, LBS_7, LBS_8, LBS_9, LBS_11, LBS_13, LBS_14, LBS_15, LBS_16, LBS_17, LBS_18, LBS_20, LBS_21, LBS_23, LBS_24, LBS_25, LBS_26, LBS_27, LBS_28, LBS_29) %>%
psych::alpha(check.keys = TRUE)##
## Reliability analysis
## Call: psych::alpha(x = ., check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.82 0.82 0.84 0.15 4.5 0.011 0.98 0.28 0.17
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.8 0.82 0.84
## Duhachek 0.8 0.82 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## LBS_1- 0.82 0.82 0.84 0.16 4.6 0.011 0.012 0.17
## LBS_2 0.82 0.81 0.84 0.15 4.3 0.011 0.013 0.17
## LBS_3 0.82 0.81 0.84 0.15 4.4 0.011 0.013 0.17
## LBS_4- 0.82 0.82 0.84 0.16 4.5 0.011 0.013 0.17
## LBS_5 0.81 0.81 0.83 0.15 4.2 0.011 0.013 0.17
## LBS_6 0.82 0.81 0.84 0.15 4.4 0.011 0.013 0.17
## LBS_7 0.82 0.81 0.84 0.15 4.3 0.011 0.013 0.17
## LBS_8 0.81 0.81 0.83 0.15 4.2 0.012 0.012 0.16
## LBS_9 0.81 0.81 0.83 0.15 4.2 0.011 0.012 0.17
## LBS_11- 0.83 0.82 0.84 0.16 4.6 0.011 0.012 0.17
## LBS_13 0.82 0.81 0.84 0.15 4.4 0.011 0.013 0.17
## LBS_14 0.81 0.81 0.83 0.15 4.3 0.011 0.013 0.17
## LBS_15 0.81 0.81 0.83 0.15 4.2 0.011 0.012 0.17
## LBS_16 0.81 0.81 0.83 0.15 4.2 0.011 0.012 0.16
## LBS_17 0.81 0.81 0.83 0.15 4.2 0.011 0.013 0.17
## LBS_18 0.82 0.81 0.83 0.15 4.3 0.011 0.013 0.17
## LBS_20- 0.82 0.82 0.84 0.16 4.5 0.011 0.013 0.17
## LBS_21 0.81 0.81 0.83 0.15 4.2 0.012 0.012 0.16
## LBS_23 0.82 0.81 0.84 0.15 4.3 0.011 0.013 0.17
## LBS_24 0.82 0.81 0.84 0.15 4.3 0.011 0.013 0.17
## LBS_25- 0.83 0.82 0.84 0.16 4.7 0.010 0.011 0.17
## LBS_26 0.82 0.81 0.83 0.15 4.3 0.011 0.012 0.17
## LBS_27 0.82 0.82 0.84 0.16 4.5 0.011 0.013 0.17
## LBS_28- 0.83 0.82 0.84 0.16 4.6 0.011 0.012 0.17
## LBS_29 0.81 0.81 0.83 0.15 4.3 0.011 0.012 0.17
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## LBS_1- 395 0.24 0.26 0.20 0.162 0.88 0.53
## LBS_2 395 0.46 0.45 0.42 0.377 0.96 0.68
## LBS_3 395 0.44 0.43 0.39 0.358 1.00 0.66
## LBS_4- 390 0.31 0.32 0.27 0.227 0.91 0.60
## LBS_5 395 0.54 0.53 0.51 0.466 1.03 0.70
## LBS_6 394 0.44 0.44 0.40 0.362 1.13 0.69
## LBS_7 392 0.45 0.45 0.42 0.378 0.99 0.63
## LBS_8 391 0.59 0.58 0.57 0.519 1.18 0.71
## LBS_9 393 0.53 0.52 0.50 0.459 1.06 0.66
## LBS_11- 394 0.23 0.24 0.18 0.143 0.86 0.61
## LBS_13 392 0.42 0.42 0.38 0.343 1.02 0.62
## LBS_14 393 0.52 0.51 0.49 0.446 0.95 0.64
## LBS_15 395 0.55 0.54 0.52 0.474 0.95 0.72
## LBS_16 394 0.54 0.53 0.51 0.466 1.09 0.70
## LBS_17 393 0.55 0.55 0.53 0.481 1.02 0.64
## LBS_18 395 0.49 0.48 0.45 0.410 0.97 0.65
## LBS_20- 394 0.35 0.36 0.32 0.270 0.85 0.63
## LBS_21 395 0.56 0.55 0.53 0.485 1.13 0.69
## LBS_23 395 0.45 0.45 0.41 0.376 1.08 0.65
## LBS_24 392 0.46 0.46 0.42 0.385 0.87 0.64
## LBS_25- 392 0.17 0.18 0.13 0.084 0.93 0.61
## LBS_26 393 0.47 0.46 0.43 0.390 0.93 0.66
## LBS_27 395 0.36 0.36 0.31 0.280 0.94 0.60
## LBS_28- 394 0.22 0.23 0.17 0.129 0.92 0.65
## LBS_29 389 0.50 0.50 0.47 0.430 0.98 0.64
##
## Non missing response frequency for each item
## 0 1 2 miss
## LBS_1 0.09 0.70 0.21 0.27
## LBS_2 0.25 0.53 0.21 0.27
## LBS_3 0.22 0.56 0.22 0.27
## LBS_4 0.14 0.63 0.23 0.28
## LBS_5 0.23 0.51 0.26 0.27
## LBS_6 0.18 0.51 0.31 0.27
## LBS_7 0.20 0.61 0.19 0.27
## LBS_8 0.18 0.46 0.36 0.28
## LBS_9 0.19 0.56 0.25 0.27
## LBS_11 0.12 0.61 0.27 0.27
## LBS_13 0.18 0.62 0.20 0.27
## LBS_14 0.23 0.60 0.18 0.27
## LBS_15 0.29 0.48 0.24 0.27
## LBS_16 0.20 0.51 0.29 0.27
## LBS_17 0.20 0.59 0.21 0.27
## LBS_18 0.23 0.57 0.20 0.27
## LBS_20 0.13 0.58 0.28 0.27
## LBS_21 0.18 0.51 0.31 0.27
## LBS_23 0.18 0.57 0.26 0.27
## LBS_24 0.28 0.58 0.15 0.27
## LBS_25 0.15 0.62 0.23 0.27
## LBS_26 0.25 0.57 0.18 0.27
## LBS_27 0.21 0.64 0.15 0.27
## LBS_28 0.18 0.57 0.26 0.27
## LBS_29 0.21 0.60 0.19 0.28# LBS .82
mydata %>%
dplyr::select(COVID_worriedcatchvirus,COVID_worriedfamilysafe,
COVID_worriedhealthcaresystem,COVID_worriedgrocerystoresfood,COVID_worriedgrocerystoresshutdown,
COVID_worriedgrocerystoresclearningsupplies,COVID_worriedtouchpublicplace,
COVID_worriedsomeonecoughs,COVID_troubleconcentrating,COVID_disturbingmentalimages,
COVID_troublesleeping) %>%
psych::alpha()##
## Reliability analysis
## Call: psych::alpha(x = .)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.9 0.9 0.91 0.44 8.8 0.0066 2.9 0.8 0.42
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.88 0.9 0.91
## Duhachek 0.89 0.9 0.91
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc)
## COVID_worriedcatchvirus 0.89 0.89 0.90
## COVID_worriedfamilysafe 0.89 0.89 0.89
## COVID_worriedhealthcaresystem 0.89 0.89 0.90
## COVID_worriedgrocerystoresfood 0.89 0.89 0.89
## COVID_worriedgrocerystoresshutdown 0.89 0.89 0.89
## COVID_worriedgrocerystoresclearningsupplies 0.89 0.89 0.89
## COVID_worriedtouchpublicplace 0.89 0.89 0.90
## COVID_worriedsomeonecoughs 0.89 0.89 0.89
## COVID_troubleconcentrating 0.89 0.89 0.89
## COVID_disturbingmentalimages 0.89 0.89 0.89
## COVID_troublesleeping 0.89 0.89 0.90
## average_r S/N alpha se var.r med.r
## COVID_worriedcatchvirus 0.45 8.2 0.0071 0.0071 0.43
## COVID_worriedfamilysafe 0.44 7.9 0.0072 0.0073 0.42
## COVID_worriedhealthcaresystem 0.45 8.1 0.0071 0.0074 0.42
## COVID_worriedgrocerystoresfood 0.44 7.9 0.0073 0.0060 0.42
## COVID_worriedgrocerystoresshutdown 0.44 7.8 0.0073 0.0059 0.42
## COVID_worriedgrocerystoresclearningsupplies 0.44 7.9 0.0073 0.0069 0.42
## COVID_worriedtouchpublicplace 0.45 8.2 0.0070 0.0068 0.43
## COVID_worriedsomeonecoughs 0.44 8.0 0.0072 0.0071 0.42
## COVID_troubleconcentrating 0.44 7.9 0.0073 0.0069 0.41
## COVID_disturbingmentalimages 0.44 8.0 0.0072 0.0067 0.43
## COVID_troublesleeping 0.45 8.2 0.0071 0.0062 0.43
##
## Item statistics
## n raw.r std.r r.cor r.drop mean
## COVID_worriedcatchvirus 398 0.67 0.68 0.63 0.59 3.0
## COVID_worriedfamilysafe 396 0.71 0.72 0.68 0.64 3.0
## COVID_worriedhealthcaresystem 394 0.69 0.69 0.65 0.61 2.9
## COVID_worriedgrocerystoresfood 395 0.72 0.72 0.69 0.65 2.8
## COVID_worriedgrocerystoresshutdown 395 0.74 0.74 0.72 0.67 2.7
## COVID_worriedgrocerystoresclearningsupplies 394 0.72 0.72 0.68 0.65 2.8
## COVID_worriedtouchpublicplace 395 0.66 0.66 0.62 0.58 3.1
## COVID_worriedsomeonecoughs 394 0.70 0.71 0.67 0.63 3.2
## COVID_troubleconcentrating 396 0.73 0.73 0.70 0.66 2.6
## COVID_disturbingmentalimages 395 0.71 0.71 0.67 0.63 2.7
## COVID_troublesleeping 395 0.68 0.68 0.64 0.60 2.6
## sd
## COVID_worriedcatchvirus 1.1
## COVID_worriedfamilysafe 1.2
## COVID_worriedhealthcaresystem 1.2
## COVID_worriedgrocerystoresfood 1.2
## COVID_worriedgrocerystoresshutdown 1.2
## COVID_worriedgrocerystoresclearningsupplies 1.1
## COVID_worriedtouchpublicplace 1.1
## COVID_worriedsomeonecoughs 1.1
## COVID_troubleconcentrating 1.1
## COVID_disturbingmentalimages 1.2
## COVID_troublesleeping 1.2
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## COVID_worriedcatchvirus 0.09 0.27 0.29 0.25 0.10 0.26
## COVID_worriedfamilysafe 0.10 0.24 0.30 0.25 0.11 0.27
## COVID_worriedhealthcaresystem 0.15 0.21 0.32 0.24 0.08 0.27
## COVID_worriedgrocerystoresfood 0.16 0.28 0.28 0.21 0.07 0.27
## COVID_worriedgrocerystoresshutdown 0.16 0.30 0.28 0.18 0.08 0.27
## COVID_worriedgrocerystoresclearningsupplies 0.14 0.26 0.32 0.20 0.08 0.27
## COVID_worriedtouchpublicplace 0.07 0.24 0.34 0.24 0.11 0.27
## COVID_worriedsomeonecoughs 0.05 0.22 0.31 0.30 0.13 0.27
## COVID_troubleconcentrating 0.16 0.32 0.28 0.20 0.05 0.27
## COVID_disturbingmentalimages 0.20 0.27 0.27 0.20 0.07 0.27
## COVID_troublesleeping 0.23 0.28 0.24 0.20 0.05 0.27#covid parent stress .90#correlation Matrix
corrplot(cor1b, method = "color",type = "upper",
p.mat = cor1$p, addCoef.col = 'black', sig.level = 0.05,
insig = "label_sig", number.cex=0.8, tl.col="black",
col=colorRampPalette(c("#94AEBC", "white", "thistle"))(50))
# level of covid restriction is weakly, positively correlated with the impact on the child
# and on the stress reported
# covid impacts on child is strongly positively correlated with covid stress
# and depression and anxiety at baseline, and negatively strongly linked
# to learning behavior scale. covid impact is weakly, positively correlated
# with anxiety at T3s
# total diagnoses is moderately, positively correlated with all variables, except
# negative with learning behavior scale score
# covid stress is moderately negatively correlated to learning behaavior scale
# and strongly positive linked to depression and anxiety at T1, and moderately
# positively linked to anxiety at T3
# baseline adhd symptoms are strongly, positively correlated to baseline anxiety,
# depresion, covid impact on child and covid parent stress, and negatively strongly
# linked to learning behavior scale scores
# so baseline covid measures (impact, stress) tend to be linked to higher anxiety
# over time but not to depression
# learning behavior scale is moderately to strongly linked to lower levels of anxiety and
# depression at T1 and T3model1 <- lm(data = mydata, anxiety_T3 ~ anxiety_T1 + Educationhousehold + Familyincome_T1r + Child_gender + Childagecategory + school.modality.f + Child_raceethnicity_T1 + COVIDrestrictionstotal + COVID_childimpact_T1 + COVIDstressscale + LBStotal + CASI_ADHD_CSum + DxTotal)
summary(model1)##
## Call:
## lm(formula = anxiety_T3 ~ anxiety_T1 + Educationhousehold + Familyincome_T1r +
## Child_gender + Childagecategory + school.modality.f + Child_raceethnicity_T1 +
## COVIDrestrictionstotal + COVID_childimpact_T1 + COVIDstressscale +
## LBStotal + CASI_ADHD_CSum + DxTotal, data = mydata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.84473 -0.20192 -0.02942 0.22881 1.10566
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.825629 0.542885 3.363 0.00104 **
## anxiety_T1 0.257247 0.108539 2.370 0.01938 *
## Educationhousehold -0.025878 0.031577 -0.820 0.41411
## Familyincome_T1r -0.023401 0.033431 -0.700 0.48530
## Child_gender 0.009407 0.069542 0.135 0.89262
## Childagecategory 0.007434 0.060774 0.122 0.90285
## school.modality.falternating 0.077728 0.090745 0.857 0.39340
## school.modality.fvirtual 0.228810 0.094925 2.410 0.01745 *
## Child_raceethnicity_T1 -0.021573 0.017822 -1.210 0.22847
## COVIDrestrictionstotal -0.002491 0.001688 -1.475 0.14270
## COVID_childimpact_T1 -0.010983 0.011485 -0.956 0.34085
## COVIDstressscale 0.015284 0.006239 2.450 0.01574 *
## LBStotal -0.025989 0.009070 -2.865 0.00492 **
## CASI_ADHD_CSum -0.014928 0.012829 -1.164 0.24690
## DxTotal -0.027795 0.036791 -0.755 0.45143
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3806 on 120 degrees of freedom
## (405 observations deleted due to missingness)
## Multiple R-squared: 0.3805, Adjusted R-squared: 0.3083
## F-statistic: 5.265 on 14 and 120 DF, p-value: 1.084e-07tab_model(model1, show.std=TRUE)| Â | anxiety T 3 | ||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Beta | CI | standardized CI | p |
| (Intercept) | 1.83 | -0.13 | 0.75 – 2.90 | -0.32 – 0.06 | 0.001 |
| anxiety T1 | 0.26 | 0.25 | 0.04 – 0.47 | 0.04 – 0.47 | 0.019 |
| Educationhousehold | -0.03 | -0.07 | -0.09 – 0.04 | -0.24 – 0.10 | 0.414 |
| Familyincome T1r | -0.02 | -0.06 | -0.09 – 0.04 | -0.25 – 0.12 | 0.485 |
| Child gender | 0.01 | 0.01 | -0.13 – 0.15 | -0.14 – 0.16 | 0.893 |
| Childagecategory | 0.01 | 0.01 | -0.11 – 0.13 | -0.16 – 0.18 | 0.903 |
|
school modality f [alternating] |
0.08 | 0.17 | -0.10 – 0.26 | -0.22 – 0.56 | 0.393 |
|
school modality f [virtual] |
0.23 | 0.50 | 0.04 – 0.42 | 0.09 – 0.91 | 0.017 |
| Child raceethnicity T1 | -0.02 | -0.11 | -0.06 – 0.01 | -0.28 – 0.07 | 0.228 |
| COVIDrestrictionstotal | -0.00 | -0.11 | -0.01 – 0.00 | -0.27 – 0.04 | 0.143 |
| COVID childimpact T1 | -0.01 | -0.09 | -0.03 – 0.01 | -0.29 – 0.10 | 0.341 |
| COVIDstressscale | 0.02 | 0.20 | 0.00 – 0.03 | 0.04 – 0.37 | 0.016 |
| LBStotal | -0.03 | -0.33 | -0.04 – -0.01 | -0.56 – -0.10 | 0.005 |
| CASI ADHD CSum | -0.01 | -0.12 | -0.04 – 0.01 | -0.31 – 0.08 | 0.247 |
| DxTotal | -0.03 | -0.07 | -0.10 – 0.05 | -0.24 – 0.11 | 0.451 |
| Observations | 135 | ||||
| R2 / R2 adjusted | 0.381 / 0.308 | ||||
calc.relimp(model1)## Response variable: anxiety_T3
## Total response variance: 0.2094583
## Analysis based on 135 observations
##
## 14 Regressors:
## Some regressors combined in groups:
## Group school.modality.f : school.modality.falternating school.modality.fvirtual
##
## Relative importance of 13 (groups of) regressors assessed:
## school.modality.f anxiety_T1 Educationhousehold Familyincome_T1r Child_gender Childagecategory Child_raceethnicity_T1 COVIDrestrictionstotal COVID_childimpact_T1 COVIDstressscale LBStotal CASI_ADHD_CSum DxTotal
##
## Proportion of variance explained by model: 38.05%
## Metrics are not normalized (rela=FALSE).
##
## Relative importance metrics:
##
## lmg
## school.modality.f 0.047150931
## anxiety_T1 0.089915089
## Educationhousehold 0.010145835
## Familyincome_T1r 0.029908876
## Child_gender 0.006605688
## Childagecategory 0.006923134
## Child_raceethnicity_T1 0.012010386
## COVIDrestrictionstotal 0.017187177
## COVID_childimpact_T1 0.008358904
## COVIDstressscale 0.051466177
## LBStotal 0.086186936
## CASI_ADHD_CSum 0.010173048
## DxTotal 0.004501692
##
## Average coefficients for different model sizes:
##
## 1group 2groups 3groups 4groups
## anxiety_T1 0.44521875 0.42964651 0.414449328 0.3993833069
## Educationhousehold -0.06555076 -0.05349584 -0.044643303 -0.0381430212
## Familyincome_T1r -0.11024324 -0.09712450 -0.085919133 -0.0762196207
## Child_gender 0.12984922 0.11524703 0.101560248 0.0886913877
## Childagecategory 0.10261550 0.08549614 0.073024170 0.0635751638
## school.modality.falternating 0.22773906 0.20253890 0.182925694 0.1667562797
## school.modality.fvirtual 0.26504065 0.26850559 0.268829458 0.2669433725
## Child_raceethnicity_T1 -0.02887463 -0.02608655 -0.024241126 -0.0229791652
## COVIDrestrictionstotal -0.00239581 -0.00279712 -0.003030606 -0.0031514013
## COVID_childimpact_T1 0.01953452 0.01345075 0.008472895 0.0043663399
## COVIDstressscale 0.02277336 0.02110372 0.019863058 0.0189204773
## LBStotal -0.03228009 -0.03103808 -0.030025724 -0.0291963016
## CASI_ADHD_CSum 0.01783673 0.01051471 0.004739235 0.0001669794
## DxTotal 0.03350987 0.01490473 0.001443286 -0.0082630156
## 5groups 6groups 7groups
## anxiety_T1 0.3842755874 0.369008439 0.353510203
## Educationhousehold -0.0333957914 -0.029975699 -0.027579211
## Familyincome_T1r -0.0677223777 -0.060197219 -0.053465713
## Child_gender 0.0766267403 0.065373884 0.054940737
## Childagecategory 0.0560158216 0.049557434 0.043657107
## school.modality.falternating 0.1528110620 0.140399231 0.129130374
## school.modality.fvirtual 0.2635904378 0.259337965 0.254607240
## Child_raceethnicity_T1 -0.0220989532 -0.021488369 -0.021085491
## COVIDrestrictionstotal -0.0031950968 -0.003185239 -0.003137676
## COVID_childimpact_T1 0.0009632031 -0.001858644 -0.004190067
## COVIDstressscale 0.0181883439 0.017606720 0.017133545
## LBStotal -0.0285160141 -0.027959326 -0.027505778
## CASI_ADHD_CSum -0.0034602657 -0.006339438 -0.008622767
## DxTotal -0.0151990091 -0.020079839 -0.023433589
## 8groups 9groups 10groups
## anxiety_T1 0.337751502 0.321745469 0.305551047
## Educationhousehold -0.025990089 -0.025053772 -0.024657399
## Familyincome_T1r -0.047386214 -0.041843820 -0.036743993
## Child_gender 0.045330665 0.036542637 0.028571529
## Childagecategory 0.037950733 0.032207337 0.026298097
## school.modality.falternating 0.118783560 0.109231999 0.100399140
## school.modality.fvirtual 0.249707012 0.244863755 0.240245351
## Child_raceethnicity_T1 -0.020855182 -0.020775053 -0.020827140
## COVIDrestrictionstotal -0.003063232 -0.002969438 -0.002861685
## COVID_childimpact_T1 -0.006100682 -0.007645662 -0.008870268
## COVIDstressscale 0.016738417 0.016398614 0.016096502
## LBStotal -0.027137723 -0.026838715 -0.026592341
## CASI_ADHD_CSum -0.010429061 -0.011851410 -0.012962591
## DxTotal -0.025653817 -0.027034580 -0.027795193
## 11groups 12groups 13groups
## anxiety_T1 0.289278714 0.273098161 0.257247293
## Educationhousehold -0.024713355 -0.025145497 -0.025878309
## Familyincome_T1r -0.032009046 -0.027576807 -0.023400715
## Child_gender 0.021406786 0.015029294 0.009407427
## Childagecategory 0.020175701 0.013860902 0.007433643
## school.modality.falternating 0.092232903 0.084690478 0.077728355
## school.modality.fvirtual 0.235976721 0.232147108 0.228810061
## Child_raceethnicity_T1 -0.020992991 -0.021250733 -0.021573337
## COVIDrestrictionstotal -0.002743999 -0.002619557 -0.002491016
## COVID_childimpact_T1 -0.009812871 -0.010507014 -0.010982903
## COVIDstressscale 0.015817835 0.015550631 0.015284463
## LBStotal -0.026381364 -0.026187052 -0.025988618
## CASI_ADHD_CSum -0.013818993 -0.014463572 -0.014928106
## DxTotal -0.028098858 -0.028067607 -0.027795000model2 <- lm(data = mydata, depression_T3 ~ depression_T1 + Educationhousehold + Familyincome_T1r + Child_gender + Childagecategory + school.modality.f + Child_raceethnicity_T1 + COVIDrestrictionstotal + COVID_childimpact_T1 + COVIDstressscale + LBStotal + CASI_ADHD_CSum + DxTotal)
summary(model2)##
## Call:
## lm(formula = depression_T3 ~ depression_T1 + Educationhousehold +
## Familyincome_T1r + Child_gender + Childagecategory + school.modality.f +
## Child_raceethnicity_T1 + COVIDrestrictionstotal + COVID_childimpact_T1 +
## COVIDstressscale + LBStotal + CASI_ADHD_CSum + DxTotal, data = mydata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.10166 -0.38413 0.02493 0.37059 1.20198
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.541031 0.712530 2.163 0.03254 *
## depression_T1 0.256603 0.107260 2.392 0.01829 *
## Educationhousehold -0.014205 0.042185 -0.337 0.73691
## Familyincome_T1r -0.132481 0.044500 -2.977 0.00352 **
## Child_gender 0.019003 0.093362 0.204 0.83906
## Childagecategory 0.076286 0.082084 0.929 0.35456
## school.modality.falternating 0.062158 0.121155 0.513 0.60886
## school.modality.fvirtual 0.073032 0.127825 0.571 0.56883
## Child_raceethnicity_T1 -0.013043 0.024042 -0.543 0.58847
## COVIDrestrictionstotal -0.002101 0.002264 -0.928 0.35527
## COVID_childimpact_T1 -0.011860 0.015333 -0.774 0.44074
## COVIDstressscale 0.013575 0.008294 1.637 0.10431
## LBStotal -0.010566 0.012225 -0.864 0.38915
## CASI_ADHD_CSum -0.012755 0.017282 -0.738 0.46194
## DxTotal -0.036171 0.049317 -0.733 0.46472
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5117 on 120 degrees of freedom
## (405 observations deleted due to missingness)
## Multiple R-squared: 0.2848, Adjusted R-squared: 0.2013
## F-statistic: 3.412 on 14 and 120 DF, p-value: 0.0001171tab_model(model2, show.std=TRUE)| Â | depression T 3 | ||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Beta | CI | standardized CI | p |
| (Intercept) | 1.54 | -0.05 | 0.13 – 2.95 | -0.25 – 0.16 | 0.033 |
| depression T1 | 0.26 | 0.25 | 0.04 – 0.47 | 0.04 – 0.46 | 0.018 |
| Educationhousehold | -0.01 | -0.03 | -0.10 – 0.07 | -0.21 – 0.15 | 0.737 |
| Familyincome T1r | -0.13 | -0.29 | -0.22 – -0.04 | -0.49 – -0.10 | 0.004 |
| Child gender | 0.02 | 0.02 | -0.17 – 0.20 | -0.15 – 0.18 | 0.839 |
| Childagecategory | 0.08 | 0.09 | -0.09 – 0.24 | -0.10 – 0.27 | 0.355 |
|
school modality f [alternating] |
0.06 | 0.11 | -0.18 – 0.30 | -0.31 – 0.53 | 0.609 |
|
school modality f [virtual] |
0.07 | 0.13 | -0.18 – 0.33 | -0.31 – 0.57 | 0.569 |
| Child raceethnicity T1 | -0.01 | -0.05 | -0.06 – 0.03 | -0.24 – 0.14 | 0.588 |
| COVIDrestrictionstotal | -0.00 | -0.08 | -0.01 – 0.00 | -0.24 – 0.09 | 0.355 |
| COVID childimpact T1 | -0.01 | -0.08 | -0.04 – 0.02 | -0.29 – 0.13 | 0.441 |
| COVIDstressscale | 0.01 | 0.14 | -0.00 – 0.03 | -0.03 – 0.32 | 0.104 |
| LBStotal | -0.01 | -0.11 | -0.03 – 0.01 | -0.35 – 0.14 | 0.389 |
| CASI ADHD CSum | -0.01 | -0.08 | -0.05 – 0.02 | -0.29 – 0.13 | 0.462 |
| DxTotal | -0.04 | -0.07 | -0.13 – 0.06 | -0.25 – 0.12 | 0.465 |
| Observations | 135 | ||||
| R2 / R2 adjusted | 0.285 / 0.201 | ||||
calc.relimp(model2)## Response variable: depression_T3
## Total response variance: 0.3278607
## Analysis based on 135 observations
##
## 14 Regressors:
## Some regressors combined in groups:
## Group school.modality.f : school.modality.falternating school.modality.fvirtual
##
## Relative importance of 13 (groups of) regressors assessed:
## school.modality.f depression_T1 Educationhousehold Familyincome_T1r Child_gender Childagecategory Child_raceethnicity_T1 COVIDrestrictionstotal COVID_childimpact_T1 COVIDstressscale LBStotal CASI_ADHD_CSum DxTotal
##
## Proportion of variance explained by model: 28.48%
## Metrics are not normalized (rela=FALSE).
##
## Relative importance metrics:
##
## lmg
## school.modality.f 0.014766154
## depression_T1 0.063644695
## Educationhousehold 0.008845919
## Familyincome_T1r 0.089033358
## Child_gender 0.004957479
## Childagecategory 0.009748902
## Child_raceethnicity_T1 0.016213084
## COVIDrestrictionstotal 0.005453117
## COVID_childimpact_T1 0.005235176
## COVIDstressscale 0.022112901
## LBStotal 0.035401830
## CASI_ADHD_CSum 0.006021568
## DxTotal 0.003321919
##
## Average coefficients for different model sizes:
##
## 1group 2groups 3groups
## depression_T1 0.362080367 0.343382706 0.328228603
## Educationhousehold -0.080338414 -0.067042499 -0.056865357
## Familyincome_T1r -0.181456644 -0.172502364 -0.164960951
## Child_gender 0.137351779 0.122424606 0.108695680
## Childagecategory 0.132125355 0.113792448 0.100664789
## school.modality.falternating 0.228005226 0.201490825 0.180666609
## school.modality.fvirtual 0.186219512 0.185990751 0.181792353
## Child_raceethnicity_T1 -0.046905228 -0.043036531 -0.039909215
## COVIDrestrictionstotal -0.001141883 -0.001603693 -0.001891763
## COVID_childimpact_T1 0.020450762 0.014281267 0.009351145
## COVIDstressscale 0.018572113 0.016892697 0.015744842
## LBStotal -0.030202206 -0.027814913 -0.025757392
## CASI_ADHD_CSum 0.020263902 0.012334512 0.006001607
## DxTotal 0.038363797 0.017891610 0.002746074
## 4groups 5groups 6groups
## depression_T1 0.3156007411 0.304808366 0.2953935702
## Educationhousehold -0.0489171954 -0.042564832 -0.0373548012
## Familyincome_T1r -0.1585479017 -0.153062407 -0.1483625556
## Child_gender 0.0959574582 0.084129358 0.0731731735
## Childagecategory 0.0911150339 0.084068318 0.0788355601
## school.modality.falternating 0.1633097525 0.148178841 0.1345783428
## school.modality.fvirtual 0.1747378781 0.165703725 0.1553476630
## Child_raceethnicity_T1 -0.0371819563 -0.034665739 -0.0322516981
## COVIDrestrictionstotal -0.0020643154 -0.002159128 -0.0022014825
## COVID_childimpact_T1 0.0053679464 0.002115583 -0.0005689237
## COVIDstressscale 0.0149572627 0.014417253 0.0140494376
## LBStotal -0.0239522496 -0.022338295 -0.0208641769
## CASI_ADHD_CSum 0.0009298009 -0.003129718 -0.0063608430
## DxTotal -0.0084994954 -0.016854905 -0.0230501838
## 7groups 8groups 9groups
## depression_T1 0.287066263 0.279660087 0.273103956
## Educationhousehold -0.032962290 -0.029155092 -0.025766467
## Familyincome_T1r -0.144348294 -0.140950562 -0.138125220
## Child_gender 0.063062772 0.053775511 0.045291094
## Childagecategory 0.074997321 0.072323479 0.070719183
## school.modality.falternating 0.122112333 0.110547274 0.099735731
## school.modality.fvirtual 0.144149436 0.132454221 0.120510008
## Child_raceethnicity_T1 -0.029870169 -0.027467280 -0.024991500
## COVIDrestrictionstotal -0.002208879 -0.002194008 -0.002166740
## COVID_childimpact_T1 -0.002811813 -0.004714364 -0.006361535
## COVIDstressscale 0.013802627 0.013641756 0.013543069
## LBStotal -0.019483882 -0.018153427 -0.016828320
## CASI_ADHD_CSum -0.008897596 -0.010834390 -0.012232971
## DxTotal -0.027619397 -0.030953166 -0.033332655
## 10groups 11groups 12groups
## depression_T1 0.267405407 0.262643134 0.258966829
## Educationhousehold -0.022673235 -0.019776944 -0.016986934
## Familyincome_T1r -0.135850658 -0.134128338 -0.132985661
## Child_gender 0.037591452 0.030659036 0.024472883
## Childagecategory 0.070190396 0.070824143 0.072779523
## school.modality.falternating 0.089573554 0.079975778 0.070863544
## school.modality.fvirtual 0.108495176 0.096534909 0.084706230
## Child_raceethnicity_T1 -0.022385801 -0.019582794 -0.016501277
## COVIDrestrictionstotal -0.002135525 -0.002108416 -0.002093859
## COVID_childimpact_T1 -0.007828521 -0.009186017 -0.010504786
## COVIDstressscale 0.013491398 0.013478836 0.013504414
## LBStotal -0.015461530 -0.014001712 -0.012391522
## CASI_ADHD_CSum -0.013127060 -0.013525387 -0.013413507
## DxTotal -0.034952144 -0.035934124 -0.036339054
## 13groups
## depression_T1 0.256602815
## Educationhousehold -0.014204931
## Familyincome_T1r -0.132480512
## Child_gender 0.019002968
## Childagecategory 0.076286244
## school.modality.falternating 0.062157907
## school.modality.fvirtual 0.073032359
## Child_raceethnicity_T1 -0.013043343
## COVIDrestrictionstotal -0.002101334
## COVID_childimpact_T1 -0.011859920
## COVIDstressscale 0.013574512
## LBStotal -0.010565907
## CASI_ADHD_CSum -0.012754640
## DxTotal -0.036171181# time-variant: attention symptoms at each time point (CASI_ADHD_CSum)
# time-invariant: final academic scores (Academicperformance_total_T4)
# without sociodemographic variables (only continuous covariate of ADHD)
predict.model2 <- '
# latent growth factors
Intercept =~ 1*depression_T1 + 1*depression_T2 + 1*depression_T3 + 1*depression_T4
Linear =~ 0*depression_T1 + 1*depression_T2 + 2*depression_T3 + 3*depression_T4
# covariates
Intercept ~ CASI_ADHD_CSum + CASI_ADHD_CSum_T2 + CASI_ADHD_CSum_T3 + CASI_ADHDCSum_T4
Linear ~ CASI_ADHD_CSum + CASI_ADHD_CSum_T2 + CASI_ADHD_CSum_T3 + CASI_ADHDCSum_T4
# observed variables regressed on the time-varying covariate
depression_T1 ~ CASI_ADHD_CSum
depression_T2 ~ CASI_ADHD_CSum_T2
depression_T3 ~ CASI_ADHD_CSum_T3
depression_T4 ~ CASI_ADHDCSum_T4'
# fit prediction model to data
predict2 <- lavaan::growth(predict.model2, data=mydata, missing = "fiml", estimator = "MLR")
lavaan::summary(predict2, fit.measures = TRUE, rsquare = TRUE)## lavaan 0.6.17 ended normally after 84 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 21
##
## Used Total
## Number of observations 57 540
## Number of missing patterns 1
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 24.329 40.271
## Degrees of freedom 9 9
## P-value (Chi-square) 0.004 0.000
## Scaling correction factor 0.604
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 96.733 85.070
## Degrees of freedom 22 22
## P-value 0.000 0.000
## Scaling correction factor 1.137
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.795 0.504
## Tucker-Lewis Index (TLI) 0.499 -0.212
##
## Robust Comparative Fit Index (CFI) 0.769
## Robust Tucker-Lewis Index (TLI) 0.436
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -105.042 -105.042
## Scaling correction factor 1.453
## for the MLR correction
## Loglikelihood unrestricted model (H1) -92.878 -92.878
## Scaling correction factor 1.198
## for the MLR correction
##
## Akaike (AIC) 252.085 252.085
## Bayesian (BIC) 294.989 294.989
## Sample-size adjusted Bayesian (SABIC) 228.974 228.974
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.173 0.247
## 90 Percent confidence interval - lower 0.092 0.152
## 90 Percent confidence interval - upper 0.257 0.351
## P-value H_0: RMSEA <= 0.050 0.011 0.001
## P-value H_0: RMSEA >= 0.080 0.968 0.997
##
## Robust RMSEA 0.176
## 90 Percent confidence interval - lower 0.099
## 90 Percent confidence interval - upper 0.257
## P-value H_0: Robust RMSEA <= 0.050 0.007
## P-value H_0: Robust RMSEA >= 0.080 0.976
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.088 0.088
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Intercept =~
## depression_T1 1.000
## depression_T2 1.000
## depression_T3 1.000
## depression_T4 1.000
## Linear =~
## depression_T1 0.000
## depression_T2 1.000
## depression_T3 2.000
## depression_T4 3.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## Intercept ~
## CASI_ADHD_CSum 0.042 0.027 1.532 0.125
## CASI_ADHD_CS_T -0.028 0.031 -0.909 0.363
## CASI_ADHD_CS_T -0.012 0.019 -0.601 0.548
## CASI_ADHDCS_T4 0.021 0.020 1.074 0.283
## Linear ~
## CASI_ADHD_CSum -0.011 0.013 -0.864 0.388
## CASI_ADHD_CS_T 0.014 0.010 1.419 0.156
## CASI_ADHD_CS_T 0.015 0.007 2.178 0.029
## CASI_ADHDCS_T4 -0.014 0.011 -1.253 0.210
## depression_T1 ~
## CASI_ADHD_CSum 0.044 0.030 1.489 0.136
## depression_T2 ~
## CASI_ADHD_CS_T 0.065 0.017 3.749 0.000
## depression_T3 ~
## CASI_ADHD_CS_T 0.057 0.015 3.738 0.000
## depression_T4 ~
## CASI_ADHDCS_T4 0.071 0.026 2.745 0.006
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## .Intercept ~~
## .Linear 0.005 0.017 0.281 0.778
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .Intercept 0.533 0.744 0.716 0.474
## .Linear -0.363 0.198 -1.835 0.067
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .depression_T1 0.168 0.064 2.605 0.009
## .depression_T2 0.125 0.022 5.655 0.000
## .depression_T3 0.099 0.029 3.471 0.001
## .depression_T4 0.171 0.045 3.780 0.000
## .Intercept 0.006 0.046 0.131 0.896
## .Linear -0.003 0.008 -0.327 0.743
##
## R-Square:
## Estimate
## depression_T1 0.276
## depression_T2 0.313
## depression_T3 0.433
## depression_T4 0.285
## Intercept 0.733
## Linear NA#standardized estimates
standardizedSolution(predict2, type = "std.all", se = TRUE, pvalue = TRUE, ci = TRUE)## lhs op rhs est.std se z pvalue ci.lower
## 1 Intercept =~ depression_T1 0.312 0.389 0.800 0.424 -0.452
## 2 Intercept =~ depression_T2 0.351 0.438 0.801 0.423 -0.508
## 3 Intercept =~ depression_T3 0.358 0.446 0.804 0.421 -0.515
## 4 Intercept =~ depression_T4 0.306 0.378 0.810 0.418 -0.434
## 5 Linear =~ depression_T1 0.000 0.000 NA NA 0.000
## 6 Linear =~ depression_T2 0.110 0.201 0.546 0.585 -0.284
## 7 Linear =~ depression_T3 0.224 0.409 0.546 0.585 -0.579
## 8 Linear =~ depression_T4 0.287 0.519 0.553 0.580 -0.730
## 9 Intercept ~ CASI_ADHD_CSum 0.827 0.863 0.959 0.338 -0.864
## 10 Intercept ~ CASI_ADHD_CSum_T2 -0.535 0.680 -0.787 0.432 -1.867
## 11 Intercept ~ CASI_ADHD_CSum_T3 -0.243 0.407 -0.597 0.550 -1.040
## 12 Intercept ~ CASI_ADHDCSum_T4 0.435 0.522 0.832 0.405 -0.589
## 13 Linear ~ CASI_ADHD_CSum -0.692 1.564 -0.443 0.658 -3.757
## 14 Linear ~ CASI_ADHD_CSum_T2 0.821 1.618 0.508 0.612 -2.349
## 15 Linear ~ CASI_ADHD_CSum_T3 1.032 1.895 0.545 0.586 -2.682
## 16 Linear ~ CASI_ADHDCSum_T4 -0.893 1.625 -0.550 0.582 -4.077
## 17 depression_T1 ~ CASI_ADHD_CSum 0.275 0.182 1.510 0.131 -0.082
## 18 depression_T2 ~ CASI_ADHD_CSum_T2 0.430 0.110 3.912 0.000 0.214
## 19 depression_T3 ~ CASI_ADHD_CSum_T3 0.431 0.115 3.752 0.000 0.206
## 20 depression_T4 ~ CASI_ADHDCSum_T4 0.448 0.166 2.698 0.007 0.123
## 21 depression_T1 ~~ depression_T1 0.724 0.215 3.368 0.001 0.303
## 22 depression_T2 ~~ depression_T2 0.687 0.141 4.871 0.000 0.410
## 23 depression_T3 ~~ depression_T3 0.567 0.154 3.686 0.000 0.266
## 24 depression_T4 ~~ depression_T4 0.715 0.160 4.479 0.000 0.402
## 25 Intercept ~~ Intercept 0.267 1.440 0.185 0.853 -2.556
## 26 Linear ~~ Linear -1.247 8.201 -0.152 0.879 -17.321
## 27 Intercept ~~ Linear 1.201 7.492 0.160 0.873 -13.483
## 28 CASI_ADHD_CSum ~~ CASI_ADHD_CSum 1.000 0.000 NA NA 1.000
## 29 CASI_ADHD_CSum ~~ CASI_ADHD_CSum_T2 0.386 0.000 NA NA 0.386
## 30 CASI_ADHD_CSum ~~ CASI_ADHD_CSum_T3 0.092 0.000 NA NA 0.092
## 31 CASI_ADHD_CSum ~~ CASI_ADHDCSum_T4 0.014 0.000 NA NA 0.014
## 32 CASI_ADHD_CSum_T2 ~~ CASI_ADHD_CSum_T2 1.000 0.000 NA NA 1.000
## 33 CASI_ADHD_CSum_T2 ~~ CASI_ADHD_CSum_T3 0.513 0.000 NA NA 0.513
## 34 CASI_ADHD_CSum_T2 ~~ CASI_ADHDCSum_T4 0.424 0.000 NA NA 0.424
## 35 CASI_ADHD_CSum_T3 ~~ CASI_ADHD_CSum_T3 1.000 0.000 NA NA 1.000
## 36 CASI_ADHD_CSum_T3 ~~ CASI_ADHDCSum_T4 0.252 0.000 NA NA 0.252
## 37 CASI_ADHDCSum_T4 ~~ CASI_ADHDCSum_T4 1.000 0.000 NA NA 1.000
## 38 depression_T1 ~1 0.000 0.000 NA NA 0.000
## 39 depression_T2 ~1 0.000 0.000 NA NA 0.000
## 40 depression_T3 ~1 0.000 0.000 NA NA 0.000
## 41 depression_T4 ~1 0.000 0.000 NA NA 0.000
## 42 CASI_ADHD_CSum ~1 4.415 0.000 NA NA 4.415
## 43 CASI_ADHD_CSum_T2 ~1 4.396 0.000 NA NA 4.396
## 44 CASI_ADHD_CSum_T3 ~1 6.227 0.000 NA NA 6.227
## 45 CASI_ADHDCSum_T4 ~1 6.477 0.000 NA NA 6.477
## 46 Intercept ~1 3.553 6.128 0.580 0.562 -8.457
## 47 Linear ~1 -7.745 14.503 -0.534 0.593 -36.171
## ci.upper
## 1 1.075
## 2 1.210
## 3 1.231
## 4 1.047
## 5 0.000
## 6 0.503
## 7 1.026
## 8 1.303
## 9 2.518
## 10 0.798
## 11 0.554
## 12 1.458
## 13 2.373
## 14 3.992
## 15 4.746
## 16 2.291
## 17 0.631
## 18 0.645
## 19 0.657
## 20 0.773
## 21 1.145
## 22 0.963
## 23 0.869
## 24 1.027
## 25 3.090
## 26 14.828
## 27 15.886
## 28 1.000
## 29 0.386
## 30 0.092
## 31 0.014
## 32 1.000
## 33 0.513
## 34 0.424
## 35 1.000
## 36 0.252
## 37 1.000
## 38 0.000
## 39 0.000
## 40 0.000
## 41 0.000
## 42 4.415
## 43 4.396
## 44 6.227
## 45 6.477
## 46 15.563
## 47 20.681#unstandardized estimates
parameterEstimates(predict2)## lhs op rhs est se z pvalue ci.lower
## 1 Intercept =~ depression_T1 1.000 0.000 NA NA 1.000
## 2 Intercept =~ depression_T2 1.000 0.000 NA NA 1.000
## 3 Intercept =~ depression_T3 1.000 0.000 NA NA 1.000
## 4 Intercept =~ depression_T4 1.000 0.000 NA NA 1.000
## 5 Linear =~ depression_T1 0.000 0.000 NA NA 0.000
## 6 Linear =~ depression_T2 1.000 0.000 NA NA 1.000
## 7 Linear =~ depression_T3 2.000 0.000 NA NA 2.000
## 8 Linear =~ depression_T4 3.000 0.000 NA NA 3.000
## 9 Intercept ~ CASI_ADHD_CSum 0.042 0.027 1.532 0.125 -0.012
## 10 Intercept ~ CASI_ADHD_CSum_T2 -0.028 0.031 -0.909 0.363 -0.090
## 11 Intercept ~ CASI_ADHD_CSum_T3 -0.012 0.019 -0.601 0.548 -0.049
## 12 Intercept ~ CASI_ADHDCSum_T4 0.021 0.020 1.074 0.283 -0.017
## 13 Linear ~ CASI_ADHD_CSum -0.011 0.013 -0.864 0.388 -0.036
## 14 Linear ~ CASI_ADHD_CSum_T2 0.014 0.010 1.419 0.156 -0.005
## 15 Linear ~ CASI_ADHD_CSum_T3 0.015 0.007 2.178 0.029 0.002
## 16 Linear ~ CASI_ADHDCSum_T4 -0.014 0.011 -1.253 0.210 -0.035
## 17 depression_T1 ~ CASI_ADHD_CSum 0.044 0.030 1.489 0.136 -0.014
## 18 depression_T2 ~ CASI_ADHD_CSum_T2 0.065 0.017 3.749 0.000 0.031
## 19 depression_T3 ~ CASI_ADHD_CSum_T3 0.057 0.015 3.738 0.000 0.027
## 20 depression_T4 ~ CASI_ADHDCSum_T4 0.071 0.026 2.745 0.006 0.020
## 21 depression_T1 ~~ depression_T1 0.168 0.064 2.605 0.009 0.041
## 22 depression_T2 ~~ depression_T2 0.125 0.022 5.655 0.000 0.082
## 23 depression_T3 ~~ depression_T3 0.099 0.029 3.471 0.001 0.043
## 24 depression_T4 ~~ depression_T4 0.171 0.045 3.780 0.000 0.083
## 25 Intercept ~~ Intercept 0.006 0.046 0.131 0.896 -0.084
## 26 Linear ~~ Linear -0.003 0.008 -0.327 0.743 -0.019
## 27 Intercept ~~ Linear 0.005 0.017 0.281 0.778 -0.029
## 28 CASI_ADHD_CSum ~~ CASI_ADHD_CSum 8.857 0.000 NA NA 8.857
## 29 CASI_ADHD_CSum ~~ CASI_ADHD_CSum_T2 3.229 0.000 NA NA 3.229
## 30 CASI_ADHD_CSum ~~ CASI_ADHD_CSum_T3 0.861 0.000 NA NA 0.861
## 31 CASI_ADHD_CSum ~~ CASI_ADHDCSum_T4 0.133 0.000 NA NA 0.133
## 32 CASI_ADHD_CSum_T2 ~~ CASI_ADHD_CSum_T2 7.917 0.000 NA NA 7.917
## 33 CASI_ADHD_CSum_T2 ~~ CASI_ADHD_CSum_T3 4.546 0.000 NA NA 4.546
## 34 CASI_ADHD_CSum_T2 ~~ CASI_ADHDCSum_T4 3.675 0.000 NA NA 3.675
## 35 CASI_ADHD_CSum_T3 ~~ CASI_ADHD_CSum_T3 9.921 0.000 NA NA 9.921
## 36 CASI_ADHD_CSum_T3 ~~ CASI_ADHDCSum_T4 2.447 0.000 NA NA 2.447
## 37 CASI_ADHDCSum_T4 ~~ CASI_ADHDCSum_T4 9.469 0.000 NA NA 9.469
## 38 depression_T1 ~1 0.000 0.000 NA NA 0.000
## 39 depression_T2 ~1 0.000 0.000 NA NA 0.000
## 40 depression_T3 ~1 0.000 0.000 NA NA 0.000
## 41 depression_T4 ~1 0.000 0.000 NA NA 0.000
## 42 CASI_ADHD_CSum ~1 13.140 0.000 NA NA 13.140
## 43 CASI_ADHD_CSum_T2 ~1 12.368 0.000 NA NA 12.368
## 44 CASI_ADHD_CSum_T3 ~1 19.614 0.000 NA NA 19.614
## 45 CASI_ADHDCSum_T4 ~1 19.930 0.000 NA NA 19.930
## 46 Intercept ~1 0.533 0.744 0.716 0.474 -0.926
## 47 Linear ~1 -0.363 0.198 -1.835 0.067 -0.750
## ci.upper
## 1 1.000
## 2 1.000
## 3 1.000
## 4 1.000
## 5 0.000
## 6 1.000
## 7 2.000
## 8 3.000
## 9 0.095
## 10 0.033
## 11 0.026
## 12 0.060
## 13 0.014
## 14 0.033
## 15 0.029
## 16 0.008
## 17 0.103
## 18 0.099
## 19 0.087
## 20 0.122
## 21 0.294
## 22 0.169
## 23 0.156
## 24 0.260
## 25 0.096
## 26 0.014
## 27 0.039
## 28 8.857
## 29 3.229
## 30 0.861
## 31 0.133
## 32 7.917
## 33 4.546
## 34 3.675
## 35 9.921
## 36 2.447
## 37 9.469
## 38 0.000
## 39 0.000
## 40 0.000
## 41 0.000
## 42 13.140
## 43 12.368
## 44 19.614
## 45 19.930
## 46 1.991
## 47 0.025semPaths(predict2,what = "path", whatLabels = "est", edge.label.cex=.7,
intercepts = TRUE, edge.color = "black", nCharNodes = 0, nCharEdges=0,
sizeLat = 6, sizeMan=9, exoVar = FALSE, exoCov = FALSE,
shapeInt = "circle", covAtResiduals = FALSE)
# predicted means and covariances for observed variables
lavaan::fitted(predict2)## $cov
## dpr_T1 dpr_T2 dpr_T3 dpr_T4 CASI_ADHD_CSm CASI_ADHD_CS_T2
## depression_T1 0.232
## depression_T2 0.035 0.182
## depression_T3 0.019 0.045 0.175
## depression_T4 0.028 0.053 0.054 0.240
## CASI_ADHD_CSum 0.663 0.440 0.237 0.157 8.857
## CASI_ADHD_CSum_T2 0.078 0.543 0.380 0.475 3.229 7.917
## CASI_ADHD_CSum_T3 -0.118 0.312 0.755 0.533 0.861 4.546
## CASI_ADHDCSum_T4 0.079 0.270 0.129 0.621 0.133 3.675
## CASI_ADHD_CS_T3 CASI_ADHDC
## depression_T1
## depression_T2
## depression_T3
## depression_T4
## CASI_ADHD_CSum
## CASI_ADHD_CSum_T2
## CASI_ADHD_CSum_T3 9.921
## CASI_ADHDCSum_T4 2.447 9.469
##
## $mean
## depression_T1 depression_T2 depression_T3 depression_T4
## 1.507 1.423 1.434 1.424
## CASI_ADHD_CSum CASI_ADHD_CSum_T2 CASI_ADHD_CSum_T3 CASI_ADHDCSum_T4
## 13.140 12.368 19.614 19.930# predicted means and correlation for factors
lavaan::lavInspect(predict2, add.labels = TRUE, "mean.lv")## Intercept Linear
## 0.923 -0.307lavaan::lavInspect(predict2, add.labels = TRUE, "cor.lv")## Intrcp Linear
## Intercept 1.000
## Linear -0.337 1.000# residuals
lavaan::residuals(predict2, type = "raw")## $type
## [1] "raw"
##
## $cov
## dpr_T1 dpr_T2 dpr_T3 dpr_T4 CASI_ADHD_CSm CASI_ADHD_CS_T2
## depression_T1 0.000
## depression_T2 0.035 -0.019
## depression_T3 -0.018 -0.036 0.029
## depression_T4 0.036 -0.004 0.014 -0.024
## CASI_ADHD_CSum 0.033 -0.036 -0.003 0.019 0.000
## CASI_ADHD_CSum_T2 0.039 -0.137 0.149 -0.108 0.000 0.000
## CASI_ADHD_CSum_T3 0.089 -0.262 0.258 -0.177 0.000 0.000
## CASI_ADHDCSum_T4 -0.166 0.102 0.133 -0.200 0.000 0.000
## CASI_ADHD_CS_T3 CASI_ADHDC
## depression_T1
## depression_T2
## depression_T3
## depression_T4
## CASI_ADHD_CSum
## CASI_ADHD_CSum_T2
## CASI_ADHD_CSum_T3 0.000
## CASI_ADHDCSum_T4 0.000 0.000
##
## $mean
## depression_T1 depression_T2 depression_T3 depression_T4
## -0.003 0.011 -0.013 0.010
## CASI_ADHD_CSum CASI_ADHD_CSum_T2 CASI_ADHD_CSum_T3 CASI_ADHDCSum_T4
## 0.000 0.000 0.000 0.000lavaan::residuals(predict2, type = "standardized.mplus")## $type
## [1] "standardized.mplus"
##
## $cov
## dpr_T1 dpr_T2 dpr_T3 dpr_T4 CASI_ADHD_CSm CASI_ADHD_CS_T2
## depression_T1 -0.016
## depression_T2 5.211 NA
## depression_T3 -0.698 NA 1.530
## depression_T4 2.157 -0.411 NA NA
## CASI_ADHD_CSum 0.494 -0.348 -0.101 0.162 0.000
## CASI_ADHD_CSum_T2 0.261 -4.854 NA -0.567 0.000 0.000
## CASI_ADHD_CSum_T3 0.580 NA NA -1.086 0.000 0.000
## CASI_ADHDCSum_T4 -1.283 2.077 NA -1.582 0.000 0.000
## CASI_ADHD_CS_T3 CASI_ADHDC
## depression_T1
## depression_T2
## depression_T3
## depression_T4
## CASI_ADHD_CSum
## CASI_ADHD_CSum_T2
## CASI_ADHD_CSum_T3 0.000
## CASI_ADHDCSum_T4 0.000 0.000
##
## $mean
## depression_T1 depression_T2 depression_T3 depression_T4
## -0.835 NA NA NA
## CASI_ADHD_CSum CASI_ADHD_CSum_T2 CASI_ADHD_CSum_T3 CASI_ADHDCSum_T4
## 0.000 0.000 0.000 0.000lavaan::residuals(predict2, type = "cor.bollen")## $type
## [1] "cor.bollen"
##
## $cov
## dpr_T1 dpr_T2 dpr_T3 dpr_T4 CASI_ADHD_CSm CASI_ADHD_CS_T2
## depression_T1 0.000
## depression_T2 0.190 0.000
## depression_T3 -0.089 -0.205 0.000
## depression_T4 0.169 0.005 0.059 0.000
## CASI_ADHD_CSum 0.023 -0.010 -0.016 0.020 0.000
## CASI_ADHD_CSum_T2 0.029 -0.095 0.093 -0.064 0.000 0.000
## CASI_ADHD_CSum_T3 0.059 -0.193 0.139 -0.102 0.000 0.000
## CASI_ADHDCSum_T4 -0.112 0.094 0.089 -0.118 0.000 0.000
## CASI_ADHD_CS_T3 CASI_ADHDC
## depression_T1
## depression_T2
## depression_T3
## depression_T4
## CASI_ADHD_CSum
## CASI_ADHD_CSum_T2
## CASI_ADHD_CSum_T3 0.000
## CASI_ADHDCSum_T4 0.000 0.000
##
## $mean
## depression_T1 depression_T2 depression_T3 depression_T4
## -0.005 0.027 -0.029 0.022
## CASI_ADHD_CSum CASI_ADHD_CSum_T2 CASI_ADHD_CSum_T3 CASI_ADHDCSum_T4
## 0.000 0.000 0.000 0.000# the mean for the intercept factor is the average outcome score at T1, and its variance
# is the random variation around this mean initial level. The linear factor mean is the average amount
# of increase in the outcome per time point, which is assumed to be the same between all pairs of adjacent
# measurement occasion# time-variant: attention symptoms at each time point (CASI_ADHD_CSum)
# time-invariant: final academic scores (Academicperformance_total_T4)
# without sociodemographic variables
predict.model4 <- '
# latent growth factors
Intercept =~ 1*anxiety_T1 + 1*anxiety_T2 + 1*anxiety_T3 + 1*anxiety_T4
Linear =~ 0*anxiety_T1 + 1*anxiety_T2 + 2*anxiety_T3 + 3*anxiety_T4
# covariates
Intercept ~ CASI_ADHD_CSum + CASI_ADHD_CSum_T2 + CASI_ADHD_CSum_T3 + CASI_ADHDCSum_T4
Linear ~ CASI_ADHD_CSum + CASI_ADHD_CSum_T2 + CASI_ADHD_CSum_T3 + CASI_ADHDCSum_T4
# observed variables regressed on the time-varying covariate
anxiety_T1 ~ CASI_ADHD_CSum
anxiety_T2 ~ CASI_ADHD_CSum_T2
anxiety_T3 ~ CASI_ADHD_CSum_T3
anxiety_T4 ~ CASI_ADHDCSum_T4'
# fit prediction model to data
predict4 <- lavaan::growth(predict.model4, data=mydata, missing = "fiml", estimator = "mlr")
lavaan::summary(predict4, fit.measures = TRUE, rsquare = TRUE)## lavaan 0.6.17 ended normally after 99 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 21
##
## Used Total
## Number of observations 57 540
## Number of missing patterns 1
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 24.097 28.571
## Degrees of freedom 9 9
## P-value (Chi-square) 0.004 0.001
## Scaling correction factor 0.843
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 139.834 137.466
## Degrees of freedom 22 22
## P-value 0.000 0.000
## Scaling correction factor 1.017
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.872 0.831
## Tucker-Lewis Index (TLI) 0.687 0.586
##
## Robust Comparative Fit Index (CFI) 0.857
## Robust Tucker-Lewis Index (TLI) 0.651
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -23.486 -23.486
## Scaling correction factor 1.275
## for the MLR correction
## Loglikelihood unrestricted model (H1) -11.437 -11.437
## Scaling correction factor 1.146
## for the MLR correction
##
## Akaike (AIC) 88.971 88.971
## Bayesian (BIC) 131.875 131.875
## Sample-size adjusted Bayesian (SABIC) 65.860 65.860
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.172 0.195
## 90 Percent confidence interval - lower 0.090 0.111
## 90 Percent confidence interval - upper 0.256 0.286
## P-value H_0: RMSEA <= 0.050 0.012 0.005
## P-value H_0: RMSEA >= 0.080 0.966 0.984
##
## Robust RMSEA 0.177
## 90 Percent confidence interval - lower 0.104
## 90 Percent confidence interval - upper 0.256
## P-value H_0: Robust RMSEA <= 0.050 0.005
## P-value H_0: Robust RMSEA >= 0.080 0.982
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.079 0.079
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Intercept =~
## anxiety_T1 1.000
## anxiety_T2 1.000
## anxiety_T3 1.000
## anxiety_T4 1.000
## Linear =~
## anxiety_T1 0.000
## anxiety_T2 1.000
## anxiety_T3 2.000
## anxiety_T4 3.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## Intercept ~
## CASI_ADHD_CSum -0.012 0.017 -0.687 0.492
## CASI_ADHD_CS_T -0.010 0.014 -0.703 0.482
## CASI_ADHD_CS_T -0.005 0.009 -0.563 0.573
## CASI_ADHDCS_T4 0.028 0.011 2.608 0.009
## Linear ~
## CASI_ADHD_CSum 0.007 0.007 1.001 0.317
## CASI_ADHD_CS_T 0.014 0.008 1.694 0.090
## CASI_ADHD_CS_T 0.006 0.005 1.120 0.263
## CASI_ADHDCS_T4 -0.018 0.007 -2.517 0.012
## anxiety_T1 ~
## CASI_ADHD_CSum 0.077 0.023 3.364 0.001
## anxiety_T2 ~
## CASI_ADHD_CS_T 0.079 0.015 5.092 0.000
## anxiety_T3 ~
## CASI_ADHD_CS_T 0.051 0.013 3.936 0.000
## anxiety_T4 ~
## CASI_ADHDCS_T4 0.060 0.020 2.971 0.003
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## .Intercept ~~
## .Linear 0.015 0.007 2.098 0.036
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .Intercept 0.375 0.300 1.249 0.212
## .Linear -0.110 0.096 -1.143 0.253
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .anxiety_T1 0.124 0.034 3.656 0.000
## .anxiety_T2 0.046 0.011 4.315 0.000
## .anxiety_T3 0.051 0.011 4.570 0.000
## .anxiety_T4 0.123 0.023 5.370 0.000
## .Intercept -0.019 0.014 -1.400 0.161
## .Linear -0.009 0.004 -2.075 0.038
##
## R-Square:
## Estimate
## anxiety_T1 0.143
## anxiety_T2 0.578
## anxiety_T3 0.484
## anxiety_T4 0.189
## Intercept NA
## Linear NA#standardized estimates
standardizedSolution(predict4, type = "std.all", pvalue = TRUE)## lhs op rhs est.std se z pvalue ci.lower
## 1 Intercept =~ anxiety_T1 NA NA NA NA NA
## 2 Intercept =~ anxiety_T2 NA NA NA NA NA
## 3 Intercept =~ anxiety_T3 NA NA NA NA NA
## 4 Intercept =~ anxiety_T4 NA NA NA NA NA
## 5 Linear =~ anxiety_T1 NA NA NA NA NA
## 6 Linear =~ anxiety_T2 NA NA NA NA NA
## 7 Linear =~ anxiety_T3 NA NA NA NA NA
## 8 Linear =~ anxiety_T4 NA NA NA NA NA
## 9 Intercept ~ CASI_ADHD_CSum NA NA NA NA NA
## 10 Intercept ~ CASI_ADHD_CSum_T2 NA NA NA NA NA
## 11 Intercept ~ CASI_ADHD_CSum_T3 NA NA NA NA NA
## 12 Intercept ~ CASI_ADHDCSum_T4 NA NA NA NA NA
## 13 Linear ~ CASI_ADHD_CSum NA NA NA NA NA
## 14 Linear ~ CASI_ADHD_CSum_T2 NA NA NA NA NA
## 15 Linear ~ CASI_ADHD_CSum_T3 NA NA NA NA NA
## 16 Linear ~ CASI_ADHDCSum_T4 NA NA NA NA NA
## 17 anxiety_T1 ~ CASI_ADHD_CSum 0.603 0.178 3.397 0.001 0.255
## 18 anxiety_T2 ~ CASI_ADHD_CSum_T2 0.674 0.112 6.037 0.000 0.455
## 19 anxiety_T3 ~ CASI_ADHD_CSum_T3 0.510 0.120 4.236 0.000 0.274
## 20 anxiety_T4 ~ CASI_ADHDCSum_T4 0.476 0.168 2.844 0.004 0.148
## 21 anxiety_T1 ~~ anxiety_T1 0.857 0.147 5.817 0.000 0.568
## 22 anxiety_T2 ~~ anxiety_T2 0.422 0.114 3.702 0.000 0.199
## 23 anxiety_T3 ~~ anxiety_T3 0.516 0.106 4.869 0.000 0.308
## 24 anxiety_T4 ~~ anxiety_T4 0.811 0.134 6.036 0.000 0.548
## 25 Intercept ~~ Intercept NA NA NA NA NA
## 26 Linear ~~ Linear NA NA NA NA NA
## 27 Intercept ~~ Linear 1.144 0.195 5.878 0.000 0.763
## 28 CASI_ADHD_CSum ~~ CASI_ADHD_CSum 1.000 0.000 NA NA 1.000
## 29 CASI_ADHD_CSum ~~ CASI_ADHD_CSum_T2 0.386 0.000 NA NA 0.386
## 30 CASI_ADHD_CSum ~~ CASI_ADHD_CSum_T3 0.092 0.000 NA NA 0.092
## 31 CASI_ADHD_CSum ~~ CASI_ADHDCSum_T4 0.014 0.000 NA NA 0.014
## 32 CASI_ADHD_CSum_T2 ~~ CASI_ADHD_CSum_T2 1.000 0.000 NA NA 1.000
## 33 CASI_ADHD_CSum_T2 ~~ CASI_ADHD_CSum_T3 0.513 0.000 NA NA 0.513
## 34 CASI_ADHD_CSum_T2 ~~ CASI_ADHDCSum_T4 0.424 0.000 NA NA 0.424
## 35 CASI_ADHD_CSum_T3 ~~ CASI_ADHD_CSum_T3 1.000 0.000 NA NA 1.000
## 36 CASI_ADHD_CSum_T3 ~~ CASI_ADHDCSum_T4 0.252 0.000 NA NA 0.252
## 37 CASI_ADHDCSum_T4 ~~ CASI_ADHDCSum_T4 1.000 0.000 NA NA 1.000
## 38 anxiety_T1 ~1 0.000 0.000 NA NA 0.000
## 39 anxiety_T2 ~1 0.000 0.000 NA NA 0.000
## 40 anxiety_T3 ~1 0.000 0.000 NA NA 0.000
## 41 anxiety_T4 ~1 0.000 0.000 NA NA 0.000
## 42 CASI_ADHD_CSum ~1 4.415 0.000 NA NA 4.415
## 43 CASI_ADHD_CSum_T2 ~1 4.396 0.000 NA NA 4.396
## 44 CASI_ADHD_CSum_T3 ~1 6.227 0.000 NA NA 6.227
## 45 CASI_ADHDCSum_T4 ~1 6.477 0.000 NA NA 6.477
## 46 Intercept ~1 NA NA NA NA NA
## 47 Linear ~1 NA NA NA NA NA
## ci.upper
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## 7 NA
## 8 NA
## 9 NA
## 10 NA
## 11 NA
## 12 NA
## 13 NA
## 14 NA
## 15 NA
## 16 NA
## 17 0.952
## 18 0.892
## 19 0.746
## 20 0.805
## 21 1.146
## 22 0.646
## 23 0.724
## 24 1.074
## 25 NA
## 26 NA
## 27 1.526
## 28 1.000
## 29 0.386
## 30 0.092
## 31 0.014
## 32 1.000
## 33 0.513
## 34 0.424
## 35 1.000
## 36 0.252
## 37 1.000
## 38 0.000
## 39 0.000
## 40 0.000
## 41 0.000
## 42 4.415
## 43 4.396
## 44 6.227
## 45 6.477
## 46 NA
## 47 NA#unstandardized estimates
parameterEstimates(predict4)## lhs op rhs est se z pvalue ci.lower
## 1 Intercept =~ anxiety_T1 1.000 0.000 NA NA 1.000
## 2 Intercept =~ anxiety_T2 1.000 0.000 NA NA 1.000
## 3 Intercept =~ anxiety_T3 1.000 0.000 NA NA 1.000
## 4 Intercept =~ anxiety_T4 1.000 0.000 NA NA 1.000
## 5 Linear =~ anxiety_T1 0.000 0.000 NA NA 0.000
## 6 Linear =~ anxiety_T2 1.000 0.000 NA NA 1.000
## 7 Linear =~ anxiety_T3 2.000 0.000 NA NA 2.000
## 8 Linear =~ anxiety_T4 3.000 0.000 NA NA 3.000
## 9 Intercept ~ CASI_ADHD_CSum -0.012 0.017 -0.687 0.492 -0.045
## 10 Intercept ~ CASI_ADHD_CSum_T2 -0.010 0.014 -0.703 0.482 -0.036
## 11 Intercept ~ CASI_ADHD_CSum_T3 -0.005 0.009 -0.563 0.573 -0.022
## 12 Intercept ~ CASI_ADHDCSum_T4 0.028 0.011 2.608 0.009 0.007
## 13 Linear ~ CASI_ADHD_CSum 0.007 0.007 1.001 0.317 -0.007
## 14 Linear ~ CASI_ADHD_CSum_T2 0.014 0.008 1.694 0.090 -0.002
## 15 Linear ~ CASI_ADHD_CSum_T3 0.006 0.005 1.120 0.263 -0.004
## 16 Linear ~ CASI_ADHDCSum_T4 -0.018 0.007 -2.517 0.012 -0.031
## 17 anxiety_T1 ~ CASI_ADHD_CSum 0.077 0.023 3.364 0.001 0.032
## 18 anxiety_T2 ~ CASI_ADHD_CSum_T2 0.079 0.015 5.092 0.000 0.048
## 19 anxiety_T3 ~ CASI_ADHD_CSum_T3 0.051 0.013 3.936 0.000 0.026
## 20 anxiety_T4 ~ CASI_ADHDCSum_T4 0.060 0.020 2.971 0.003 0.021
## 21 anxiety_T1 ~~ anxiety_T1 0.124 0.034 3.656 0.000 0.057
## 22 anxiety_T2 ~~ anxiety_T2 0.046 0.011 4.315 0.000 0.025
## 23 anxiety_T3 ~~ anxiety_T3 0.051 0.011 4.570 0.000 0.029
## 24 anxiety_T4 ~~ anxiety_T4 0.123 0.023 5.370 0.000 0.078
## 25 Intercept ~~ Intercept -0.019 0.014 -1.400 0.161 -0.046
## 26 Linear ~~ Linear -0.009 0.004 -2.075 0.038 -0.018
## 27 Intercept ~~ Linear 0.015 0.007 2.098 0.036 0.001
## 28 CASI_ADHD_CSum ~~ CASI_ADHD_CSum 8.857 0.000 NA NA 8.857
## 29 CASI_ADHD_CSum ~~ CASI_ADHD_CSum_T2 3.229 0.000 NA NA 3.229
## 30 CASI_ADHD_CSum ~~ CASI_ADHD_CSum_T3 0.861 0.000 NA NA 0.861
## 31 CASI_ADHD_CSum ~~ CASI_ADHDCSum_T4 0.133 0.000 NA NA 0.133
## 32 CASI_ADHD_CSum_T2 ~~ CASI_ADHD_CSum_T2 7.917 0.000 NA NA 7.917
## 33 CASI_ADHD_CSum_T2 ~~ CASI_ADHD_CSum_T3 4.546 0.000 NA NA 4.546
## 34 CASI_ADHD_CSum_T2 ~~ CASI_ADHDCSum_T4 3.675 0.000 NA NA 3.675
## 35 CASI_ADHD_CSum_T3 ~~ CASI_ADHD_CSum_T3 9.921 0.000 NA NA 9.921
## 36 CASI_ADHD_CSum_T3 ~~ CASI_ADHDCSum_T4 2.447 0.000 NA NA 2.447
## 37 CASI_ADHDCSum_T4 ~~ CASI_ADHDCSum_T4 9.469 0.000 NA NA 9.469
## 38 anxiety_T1 ~1 0.000 0.000 NA NA 0.000
## 39 anxiety_T2 ~1 0.000 0.000 NA NA 0.000
## 40 anxiety_T3 ~1 0.000 0.000 NA NA 0.000
## 41 anxiety_T4 ~1 0.000 0.000 NA NA 0.000
## 42 CASI_ADHD_CSum ~1 13.140 0.000 NA NA 13.140
## 43 CASI_ADHD_CSum_T2 ~1 12.368 0.000 NA NA 12.368
## 44 CASI_ADHD_CSum_T3 ~1 19.614 0.000 NA NA 19.614
## 45 CASI_ADHDCSum_T4 ~1 19.930 0.000 NA NA 19.930
## 46 Intercept ~1 0.375 0.300 1.249 0.212 -0.213
## 47 Linear ~1 -0.110 0.096 -1.143 0.253 -0.298
## ci.upper
## 1 1.000
## 2 1.000
## 3 1.000
## 4 1.000
## 5 0.000
## 6 1.000
## 7 2.000
## 8 3.000
## 9 0.022
## 10 0.017
## 11 0.012
## 12 0.049
## 13 0.022
## 14 0.030
## 15 0.015
## 16 -0.004
## 17 0.122
## 18 0.109
## 19 0.076
## 20 0.100
## 21 0.190
## 22 0.066
## 23 0.073
## 24 0.168
## 25 0.008
## 26 -0.001
## 27 0.029
## 28 8.857
## 29 3.229
## 30 0.861
## 31 0.133
## 32 7.917
## 33 4.546
## 34 3.675
## 35 9.921
## 36 2.447
## 37 9.469
## 38 0.000
## 39 0.000
## 40 0.000
## 41 0.000
## 42 13.140
## 43 12.368
## 44 19.614
## 45 19.930
## 46 0.962
## 47 0.079semPaths(predict4,what = "path", whatLabels = "est", edge.label.cex=.7,
intercepts = TRUE, edge.color = "black", nCharNodes = 0, nCharEdges=0,
sizeLat = 6, sizeMan=9, exoVar = FALSE, exoCov = FALSE,
shapeInt = "circle", covAtResiduals = FALSE)
# predicted means and covariances for observed variables
lavaan::fitted(predict4)## $cov
## anx_T1 anx_T2 anx_T3 anx_T4 CASI_ADHD_CSm CASI_ADHD_CS_T2
## anxiety_T1 0.144
## anxiety_T2 0.014 0.108
## anxiety_T3 0.017 0.042 0.099
## anxiety_T4 0.048 0.058 0.028 0.152
## CASI_ADHD_CSum 0.547 0.233 0.136 0.214 8.857
## CASI_ADHD_CSum_T2 0.216 0.686 0.389 0.474 3.229 7.917
## CASI_ADHD_CSum_T3 0.033 0.407 0.637 0.361 0.861 4.546
## CASI_ADHDCSum_T4 0.227 0.405 0.139 0.485 0.133 3.675
## CASI_ADHD_CS_T3 CASI_ADHDC
## anxiety_T1
## anxiety_T2
## anxiety_T3
## anxiety_T4
## CASI_ADHD_CSum
## CASI_ADHD_CSum_T2
## CASI_ADHD_CSum_T3 9.921
## CASI_ADHDCSum_T4 2.447 9.469
##
## $mean
## anxiety_T1 anxiety_T2 anxiety_T3 anxiety_T4
## 1.578 1.459 1.403 1.526
## CASI_ADHD_CSum CASI_ADHD_CSum_T2 CASI_ADHD_CSum_T3 CASI_ADHDCSum_T4
## 13.140 12.368 19.614 19.930# predicted means for factors
lavaan::lavInspect(predict4, add.labels = TRUE, "mean.lv")## Intercept Linear
## 0.567 -0.081lavaan::lavInspect(predict4, add.labels = TRUE, "cor.lv")## Intrcp Linear
## Intercept 1
## Linear NaN 1# residuals
lavaan::residuals(predict4, type = "raw")## $type
## [1] "raw"
##
## $cov
## anx_T1 anx_T2 anx_T3 anx_T4 CASI_ADHD_CSm CASI_ADHD_CS_T2
## anxiety_T1 -0.018
## anxiety_T2 0.008 -0.005
## anxiety_T3 0.000 -0.002 0.020
## anxiety_T4 -0.010 -0.002 -0.006 -0.019
## CASI_ADHD_CSum -0.013 0.033 -0.058 0.063 0.000
## CASI_ADHD_CSum_T2 0.062 -0.045 0.024 0.002 0.000 0.000
## CASI_ADHD_CSum_T3 0.167 -0.175 0.185 -0.140 0.000 0.000
## CASI_ADHDCSum_T4 -0.126 0.005 0.144 -0.237 0.000 0.000
## CASI_ADHD_CS_T3 CASI_ADHDC
## anxiety_T1
## anxiety_T2
## anxiety_T3
## anxiety_T4
## CASI_ADHD_CSum
## CASI_ADHD_CSum_T2
## CASI_ADHD_CSum_T3 0.000
## CASI_ADHDCSum_T4 0.000 0.000
##
## $mean
## anxiety_T1 anxiety_T2 anxiety_T3 anxiety_T4
## 0.001 0.004 -0.009 0.012
## CASI_ADHD_CSum CASI_ADHD_CSum_T2 CASI_ADHD_CSum_T3 CASI_ADHDCSum_T4
## 0.000 0.000 0.000 0.000lavaan::residuals(predict4, type = "cor.bollen")## $type
## [1] "cor.bollen"
##
## $cov
## anx_T1 anx_T2 anx_T3 anx_T4 CASI_ADHD_CSm CASI_ADHD_CS_T2
## anxiety_T1 0.000
## anxiety_T2 0.077 0.000
## anxiety_T3 0.001 -0.041 0.000
## anxiety_T4 -0.033 0.031 -0.052 0.000
## CASI_ADHD_CSum 0.022 0.040 -0.069 0.071 0.000
## CASI_ADHD_CSum_T2 0.077 -0.032 -0.013 0.032 0.000 0.000
## CASI_ADHD_CSum_T3 0.151 -0.163 0.116 -0.101 0.000 0.000
## CASI_ADHDCSum_T4 -0.101 0.016 0.124 -0.183 0.000 0.000
## CASI_ADHD_CS_T3 CASI_ADHDC
## anxiety_T1
## anxiety_T2
## anxiety_T3
## anxiety_T4
## CASI_ADHD_CSum
## CASI_ADHD_CSum_T2
## CASI_ADHD_CSum_T3 0.000
## CASI_ADHDCSum_T4 0.000 0.000
##
## $mean
## anxiety_T1 anxiety_T2 anxiety_T3 anxiety_T4
## 0.003 0.011 -0.027 0.033
## CASI_ADHD_CSum CASI_ADHD_CSum_T2 CASI_ADHD_CSum_T3 CASI_ADHDCSum_T4
## 0.000 0.000 0.000 0.000#########################################################################################################
#########################################################################################################
#########################################################################################################
# THE FOLLOWING IS ALL SUPPLEMENTAL MATERIAL AND TESTING VARIOUS MODEL APPROACHES
# ONLY KEEPING IN HERE FOR A RECORD/PAPER TRAIL, BUT IGNORE IT UNLESS REVIEWERS ASK FOR SOMETHING
#########################################################################################################
#########################################################################################################
#########################################################################################################
#########################################################################################################
# 3 step model fitting for depression #
#########################################################################################################
### MODEL 1 - no growth (intercept only) "random intercept-only model" - predicts no growth
# Loadings for all indicators on the intercept factor are fixed to equal 1.0.
# Intercept represents the factor mean, which is also the predicted average depression score
# over all years 1-4
noGrowth.model <- '
# specify intercept
# fix all loadings to 1.0
Intercept =~ 1*depression_T1 + 1*depression_T2 + 1*depression_T3 + 1*depression_T4
# fix error variance for r1 to zero
depression_T1 ~~ 0*depression_T1 '
#########################################################################################################
### MODEL 2 - latent basis growth model ("level and shape model")
# (curve fitting, level and shape)
# two growth factors: intercept (level) and shape
# loadings of all repeated measures of depression are fixed to equal 1.0, like in the
# random intercept-only model above. here, onnly looking at T1 to T4 change (overall shape)
# the basis model predicts growth trajectories that are not strictly linear - allows for
# linear or curvilinear change. the shape factor will represent the pattern
# this is called "non-linear curve fitting"
# unstandardized loadings for the shape factor are called basis coefficients
# two of these are fixed to equal constraints:
# one is specifying T1 as 0 defines the intercept [depression] factor mean)
# second is defining the random variation around the initial level
# the basis coefficients define the shape factor mean as the average change in depression
# between T1 and T4. it also scales the growth factor Shape so that a 1-unit change in
# time refers to the whole period of observation, or T1-T4 inclusive. so the loadings are
# interpreted as proportions of the total overall change that has occurred up to and
# including the corresponding measurement
# i.e., .70 for T3 would mean that .70, or 70% of the total increase in depression from T1-T4
# has occurred by T3
# the predicted average depression score is the sum of the initial mean at T1 plus the proportion
# of total change over T1-4 that has occurred up to and including the point of measurement
# the covariance represents the association between intercept and shape
# covariance indicates that higher initial levels of depression at T1 predict a higher rate of
# subsequent change between T1-4. so those who start at higher levels change the most over time
# a negative covariance indicates the opposite: Higher initial standing predicts less change over 4 timepoints
# a factor covariance of zero indicates that initial level has nothing to do with the rate of subsequent change
# maccallum-rmsea for model 2
# exact fit test
# power at N = 432
semTools::findRMSEApower(0, .05, 4, 432, .05, 1)## [1] 0.3437725# minimum N for power at least .80
semTools::findRMSEAsamplesize(0, .05, 4, .80, .05, 1)## [1] 1195basis.model <- '
Intercept =~ 1*depression_T1 + 1*depression_T2 + 1*depression_T3 + 1*depression_T4
# specify shape, first and last loadings fixed
Shape =~ 0*depression_T1 + depression_T2 + depression_T3 + 1*depression_T4
depression_T1 ~~ 0*depression_T1 '
#########################################################################################################
### MODEL 3 - linear growth model with only continuous covariates
# these assume change is strictly linear - essentially constrained versions of basis growth models
# but on the same variables. the coefficients are all fixed to equal constraints that correspond
# to times of measurement (none are free parameters)
# the mean for the intercept factor is the average depression score at T1, and its variance
# is the random variation around this mean initial level. The linear factor mean is the average amount
# of increase in depression per time point, which is assumed to be the same between all pairs of adjacent
# measurement occasions
linear.model <- '
Intercept =~ 1*depression_T1 + 1*depression_T2 + 1*depression_T3 + 1*depression_T4
# all loadings fixed to constants
Linear =~ 0*depression_T1 + 1*depression_T2 + 2*depression_T3 + 3*depression_T4
depression_T1 ~~ 0*depression_T1
# TVCs
depression_T1 ~ CASI_ADHD_CSum
depression_T2 ~ CASI_ADHD_CSum_T2
depression_T3 ~ CASI_ADHD_CSum_T3
depression_T4 ~ CASI_ADHDCSum_T4'
# fit model 1 to data
noGrowth <- lavaan::growth(noGrowth.model, data=mydata, missing = "fiml", estimator = "mlr")
# fit model 2 to data
basis <- lavaan::growth(basis.model, data=mydata, missing = "fiml", estimator = "mlr")
# fit model 3 to data
linear <- lavaan::growth(linear.model, data=mydata, missing = "fiml", estimator = "mlr")
#########################################################################################################
# model chi-squares and chi-square difference tests
anova(noGrowth, basis)##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan NOTE:
## The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference
## test is a function of two standard (not robust) statistics.
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## basis 4 1522.3 1563.7 6.4599
## noGrowth 9 1688.2 1708.9 182.3534 163.07 5 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(basis, linear)##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan NOTE:
## The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference
## test is a function of two standard (not robust) statistics.
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## basis 4 1522.32 1563.67 6.4599
## linear 18 264.91 289.43 55.1570 41.232 14 0.0001635 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# model 1 parameter estimates, global fit statistics,
# residuals
lavaan::summary(noGrowth, fit.measures = TRUE, estimates = TRUE)## lavaan 0.6.17 ended normally after 23 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 5
##
## Used Total
## Number of observations 462 540
## Number of missing patterns 14
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 182.353 168.281
## Degrees of freedom 9 9
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.084
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 59.362 50.010
## Degrees of freedom 6 6
## P-value 0.000 0.000
## Scaling correction factor 1.187
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.000 0.000
## Tucker-Lewis Index (TLI) -1.166 -1.413
##
## Robust Comparative Fit Index (CFI) 0.000
## Robust Tucker-Lewis Index (TLI) -0.407
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -839.105 -839.105
## Scaling correction factor 1.009
## for the MLR correction
## Loglikelihood unrestricted model (H1) -747.929 -747.929
## Scaling correction factor 1.057
## for the MLR correction
##
## Akaike (AIC) 1688.211 1688.211
## Bayesian (BIC) 1708.889 1708.889
## Sample-size adjusted Bayesian (SABIC) 1693.020 1693.020
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.204 0.196
## 90 Percent confidence interval - lower 0.179 0.171
## 90 Percent confidence interval - upper 0.231 0.221
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 1.000 1.000
##
## Robust RMSEA 0.430
## 90 Percent confidence interval - lower 0.362
## 90 Percent confidence interval - upper 0.502
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.866 0.866
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Intercept =~
## depression_T1 1.000
## depression_T2 1.000
## depression_T3 1.000
## depression_T4 1.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## Intercept 1.199 0.032 37.043 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .depression_T1 0.000
## .depression_T2 0.333 0.047 7.009 0.000
## .depression_T3 0.475 0.055 8.566 0.000
## .depression_T4 0.390 0.056 6.992 0.000
## Intercept 0.457 0.027 17.042 0.000lavaan::fitted(noGrowth)## $cov
## dpr_T1 dpr_T2 dpr_T3 dpr_T4
## depression_T1 0.457
## depression_T2 0.457 0.790
## depression_T3 0.457 0.457 0.932
## depression_T4 0.457 0.457 0.457 0.847
##
## $mean
## depression_T1 depression_T2 depression_T3 depression_T4
## 1.199 1.199 1.199 1.199lavaan::residuals(noGrowth, type = "standardized")## $type
## [1] "standardized"
##
## $cov
## dpr_T1 dpr_T2 dpr_T3 dpr_T4
## depression_T1 0.894
## depression_T2 -3.667 -2.505
## depression_T3 -3.643 -4.853 -2.493
## depression_T4 -3.194 -3.718 -3.676 -2.107
##
## $mean
## depression_T1 depression_T2 depression_T3 depression_T4
## -0.363 1.161 -0.819 0.929lavaan::residuals(noGrowth, type = "cor.bollen")## $type
## [1] "cor.bollen"
##
## $cov
## dpr_T1 dpr_T2 dpr_T3 dpr_T4
## depression_T1 0.000
## depression_T2 -0.308 0.000
## depression_T3 -0.301 -0.285 0.000
## depression_T4 -0.253 -0.123 -0.089 0.000
##
## $mean
## depression_T1 depression_T2 depression_T3 depression_T4
## -0.005 0.108 -0.068 0.090#standardized estimates
standardizedSolution(noGrowth, type = "std.all", pvalue = TRUE)## lhs op rhs est.std se z pvalue ci.lower ci.upper
## 1 Intercept =~ depression_T1 1.000 0.000 NA NA 1.000 1.000
## 2 Intercept =~ depression_T2 0.761 0.023 33.804 0 0.717 0.805
## 3 Intercept =~ depression_T3 0.700 0.021 32.650 0 0.658 0.742
## 4 Intercept =~ depression_T4 0.734 0.025 29.277 0 0.685 0.784
## 5 depression_T1 ~~ depression_T1 0.000 0.000 NA NA 0.000 0.000
## 6 depression_T2 ~~ depression_T2 0.421 0.034 12.309 0 0.354 0.488
## 7 depression_T3 ~~ depression_T3 0.510 0.030 16.964 0 0.451 0.568
## 8 depression_T4 ~~ depression_T4 0.461 0.037 12.496 0 0.388 0.533
## 9 Intercept ~~ Intercept 1.000 0.000 NA NA 1.000 1.000
## 10 depression_T1 ~1 0.000 0.000 NA NA 0.000 0.000
## 11 depression_T2 ~1 0.000 0.000 NA NA 0.000 0.000
## 12 depression_T3 ~1 0.000 0.000 NA NA 0.000 0.000
## 13 depression_T4 ~1 0.000 0.000 NA NA 0.000 0.000
## 14 Intercept ~1 1.773 0.075 23.720 0 1.626 1.919#unstandardized estimates
parameterEstimates(noGrowth)## lhs op rhs est se z pvalue ci.lower ci.upper
## 1 Intercept =~ depression_T1 1.000 0.000 NA NA 1.000 1.000
## 2 Intercept =~ depression_T2 1.000 0.000 NA NA 1.000 1.000
## 3 Intercept =~ depression_T3 1.000 0.000 NA NA 1.000 1.000
## 4 Intercept =~ depression_T4 1.000 0.000 NA NA 1.000 1.000
## 5 depression_T1 ~~ depression_T1 0.000 0.000 NA NA 0.000 0.000
## 6 depression_T2 ~~ depression_T2 0.333 0.047 7.009 0 0.240 0.426
## 7 depression_T3 ~~ depression_T3 0.475 0.055 8.566 0 0.366 0.584
## 8 depression_T4 ~~ depression_T4 0.390 0.056 6.992 0 0.281 0.500
## 9 Intercept ~~ Intercept 0.457 0.027 17.042 0 0.405 0.510
## 10 depression_T1 ~1 0.000 0.000 NA NA 0.000 0.000
## 11 depression_T2 ~1 0.000 0.000 NA NA 0.000 0.000
## 12 depression_T3 ~1 0.000 0.000 NA NA 0.000 0.000
## 13 depression_T4 ~1 0.000 0.000 NA NA 0.000 0.000
## 14 Intercept ~1 1.199 0.032 37.043 0 1.135 1.262# model 2 parameter estimates, global fit statistics,
# residuals
# retained model
lavaan::summary(basis, fit.measures = TRUE, rsquare = TRUE)## lavaan 0.6.17 ended normally after 36 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 10
##
## Used Total
## Number of observations 462 540
## Number of missing patterns 14
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 6.460 5.927
## Degrees of freedom 4 4
## P-value (Chi-square) 0.167 0.205
## Scaling correction factor 1.090
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 59.362 50.010
## Degrees of freedom 6 6
## P-value 0.000 0.000
## Scaling correction factor 1.187
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.954 0.956
## Tucker-Lewis Index (TLI) 0.931 0.934
##
## Robust Comparative Fit Index (CFI) 0.977
## Robust Tucker-Lewis Index (TLI) 0.965
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -751.159 -751.159
## Scaling correction factor 1.044
## for the MLR correction
## Loglikelihood unrestricted model (H1) -747.929 -747.929
## Scaling correction factor 1.057
## for the MLR correction
##
## Akaike (AIC) 1522.318 1522.318
## Bayesian (BIC) 1563.673 1563.673
## Sample-size adjusted Bayesian (SABIC) 1531.936 1531.936
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.036 0.032
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.086 0.081
## P-value H_0: RMSEA <= 0.050 0.605 0.664
## P-value H_0: RMSEA >= 0.080 0.079 0.053
##
## Robust RMSEA 0.068
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.233
## P-value H_0: Robust RMSEA <= 0.050 0.359
## P-value H_0: Robust RMSEA >= 0.080 0.550
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.066 0.066
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Intercept =~
## depression_T1 1.000
## depression_T2 1.000
## depression_T3 1.000
## depression_T4 1.000
## Shape =~
## depression_T1 0.000
## depression_T2 1.056 0.193 5.472 0.000
## depression_T3 1.243 0.231 5.371 0.000
## depression_T4 1.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## Intercept ~~
## Shape -0.277 0.046 -6.028 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## Intercept 1.199 0.034 34.854 0.000
## Shape 0.018 0.039 0.466 0.641
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .depression_T1 0.000
## .depression_T2 0.176 0.027 6.623 0.000
## .depression_T3 0.255 0.049 5.232 0.000
## .depression_T4 0.241 0.045 5.368 0.000
## Intercept 0.487 0.029 16.572 0.000
## Shape 0.205 0.064 3.198 0.001
##
## R-Square:
## Estimate
## depression_T1 1.000
## depression_T2 0.427
## depression_T3 0.312
## depression_T4 0.365lavaan::standardizedSolution(basis)## lhs op rhs est.std se z pvalue ci.lower
## 1 Intercept =~ depression_T1 1.000 0.000 NA NA 1.000
## 2 Intercept =~ depression_T2 1.259 0.085 14.802 0.000 1.092
## 3 Intercept =~ depression_T3 1.145 0.070 16.279 0.000 1.007
## 4 Intercept =~ depression_T4 1.132 0.073 15.476 0.000 0.989
## 5 Shape =~ depression_T1 0.000 0.000 NA NA 0.000
## 6 Shape =~ depression_T2 0.862 0.151 5.726 0.000 0.567
## 7 Shape =~ depression_T3 0.923 0.163 5.658 0.000 0.603
## 8 Shape =~ depression_T4 0.734 0.144 5.103 0.000 0.452
## 9 depression_T1 ~~ depression_T1 0.000 0.000 NA NA 0.000
## 10 depression_T2 ~~ depression_T2 0.573 0.098 5.871 0.000 0.382
## 11 depression_T3 ~~ depression_T3 0.688 0.101 6.785 0.000 0.489
## 12 depression_T4 ~~ depression_T4 0.635 0.087 7.270 0.000 0.464
## 13 Intercept ~~ Intercept 1.000 0.000 NA NA 1.000
## 14 Shape ~~ Shape 1.000 0.000 NA NA 1.000
## 15 Intercept ~~ Shape -0.876 0.052 -16.973 0.000 -0.977
## 16 depression_T1 ~1 0.000 0.000 NA NA 0.000
## 17 depression_T2 ~1 0.000 0.000 NA NA 0.000
## 18 depression_T3 ~1 0.000 0.000 NA NA 0.000
## 19 depression_T4 ~1 0.000 0.000 NA NA 0.000
## 20 Intercept ~1 1.718 0.074 23.101 0.000 1.572
## 21 Shape ~1 0.040 0.085 0.472 0.637 -0.127
## ci.upper
## 1 1.000
## 2 1.425
## 3 1.283
## 4 1.276
## 5 0.000
## 6 1.157
## 7 1.243
## 8 1.016
## 9 0.000
## 10 0.764
## 11 0.886
## 12 0.806
## 13 1.000
## 14 1.000
## 15 -0.775
## 16 0.000
## 17 0.000
## 18 0.000
## 19 0.000
## 20 1.864
## 21 0.208#print(lavaan::parameterEstimates(basis), nd = 5)
#standardized estimates
standardizedSolution(basis, type = "std.all", se= TRUE, pvalue = TRUE)## lhs op rhs est.std se z pvalue ci.lower
## 1 Intercept =~ depression_T1 1.000 0.000 NA NA 1.000
## 2 Intercept =~ depression_T2 1.259 0.085 14.802 0.000 1.092
## 3 Intercept =~ depression_T3 1.145 0.070 16.279 0.000 1.007
## 4 Intercept =~ depression_T4 1.132 0.073 15.476 0.000 0.989
## 5 Shape =~ depression_T1 0.000 0.000 NA NA 0.000
## 6 Shape =~ depression_T2 0.862 0.151 5.726 0.000 0.567
## 7 Shape =~ depression_T3 0.923 0.163 5.658 0.000 0.603
## 8 Shape =~ depression_T4 0.734 0.144 5.103 0.000 0.452
## 9 depression_T1 ~~ depression_T1 0.000 0.000 NA NA 0.000
## 10 depression_T2 ~~ depression_T2 0.573 0.098 5.871 0.000 0.382
## 11 depression_T3 ~~ depression_T3 0.688 0.101 6.785 0.000 0.489
## 12 depression_T4 ~~ depression_T4 0.635 0.087 7.270 0.000 0.464
## 13 Intercept ~~ Intercept 1.000 0.000 NA NA 1.000
## 14 Shape ~~ Shape 1.000 0.000 NA NA 1.000
## 15 Intercept ~~ Shape -0.876 0.052 -16.973 0.000 -0.977
## 16 depression_T1 ~1 0.000 0.000 NA NA 0.000
## 17 depression_T2 ~1 0.000 0.000 NA NA 0.000
## 18 depression_T3 ~1 0.000 0.000 NA NA 0.000
## 19 depression_T4 ~1 0.000 0.000 NA NA 0.000
## 20 Intercept ~1 1.718 0.074 23.101 0.000 1.572
## 21 Shape ~1 0.040 0.085 0.472 0.637 -0.127
## ci.upper
## 1 1.000
## 2 1.425
## 3 1.283
## 4 1.276
## 5 0.000
## 6 1.157
## 7 1.243
## 8 1.016
## 9 0.000
## 10 0.764
## 11 0.886
## 12 0.806
## 13 1.000
## 14 1.000
## 15 -0.775
## 16 0.000
## 17 0.000
## 18 0.000
## 19 0.000
## 20 1.864
## 21 0.208#unstandardized estimates
parameterEstimates(basis)## lhs op rhs est se z pvalue ci.lower ci.upper
## 1 Intercept =~ depression_T1 1.000 0.000 NA NA 1.000 1.000
## 2 Intercept =~ depression_T2 1.000 0.000 NA NA 1.000 1.000
## 3 Intercept =~ depression_T3 1.000 0.000 NA NA 1.000 1.000
## 4 Intercept =~ depression_T4 1.000 0.000 NA NA 1.000 1.000
## 5 Shape =~ depression_T1 0.000 0.000 NA NA 0.000 0.000
## 6 Shape =~ depression_T2 1.056 0.193 5.472 0.000 0.678 1.435
## 7 Shape =~ depression_T3 1.243 0.231 5.371 0.000 0.789 1.696
## 8 Shape =~ depression_T4 1.000 0.000 NA NA 1.000 1.000
## 9 depression_T1 ~~ depression_T1 0.000 0.000 NA NA 0.000 0.000
## 10 depression_T2 ~~ depression_T2 0.176 0.027 6.623 0.000 0.124 0.228
## 11 depression_T3 ~~ depression_T3 0.255 0.049 5.232 0.000 0.160 0.351
## 12 depression_T4 ~~ depression_T4 0.241 0.045 5.368 0.000 0.153 0.329
## 13 Intercept ~~ Intercept 0.487 0.029 16.572 0.000 0.430 0.545
## 14 Shape ~~ Shape 0.205 0.064 3.198 0.001 0.079 0.330
## 15 Intercept ~~ Shape -0.277 0.046 -6.028 0.000 -0.367 -0.187
## 16 depression_T1 ~1 0.000 0.000 NA NA 0.000 0.000
## 17 depression_T2 ~1 0.000 0.000 NA NA 0.000 0.000
## 18 depression_T3 ~1 0.000 0.000 NA NA 0.000 0.000
## 19 depression_T4 ~1 0.000 0.000 NA NA 0.000 0.000
## 20 Intercept ~1 1.199 0.034 34.854 0.000 1.132 1.267
## 21 Shape ~1 0.018 0.039 0.466 0.641 -0.058 0.095# implied covariances and means for observed variables
lavaan::fitted(basis)## $cov
## dpr_T1 dpr_T2 dpr_T3 dpr_T4
## depression_T1 0.487
## depression_T2 0.195 0.307
## depression_T3 0.143 0.120 0.371
## depression_T4 0.210 0.135 0.121 0.380
##
## $mean
## depression_T1 depression_T2 depression_T3 depression_T4
## 1.199 1.218 1.222 1.217# implied means for latent growth factors
lavaan::lavInspect(basis, add.labels = TRUE, "mean.lv")## Intercept Shape
## 1.199 0.018# residuals
lavaan::residuals(basis, type = "raw")## $type
## [1] "raw"
##
## $cov
## dpr_T1 dpr_T2 dpr_T3 dpr_T4
## depression_T1 0.000
## depression_T2 -0.025 -0.018
## depression_T3 0.028 -0.038 0.008
## depression_T4 0.001 0.012 0.043 0.014
##
## $mean
## depression_T1 depression_T2 depression_T3 depression_T4
## -0.004 0.038 -0.065 0.038lavaan::residuals(basis, type = "standardized")## $type
## [1] "standardized"
##
## $cov
## dpr_T1 dpr_T2 dpr_T3 dpr_T4
## depression_T1 -0.264
## depression_T2 -1.741 -1.125
## depression_T3 2.285 -1.622 1.089
## depression_T4 0.049 0.274 1.349 0.798
##
## $mean
## depression_T1 depression_T2 depression_T3 depression_T4
## -1.808 1.119 -1.734 0.806lavaan::residuals(basis, type = "cor.bollen")## $type
## [1] "cor.bollen"
##
## $cov
## dpr_T1 dpr_T2 dpr_T3 dpr_T4
## depression_T1 0.000
## depression_T2 -0.051 0.000
## depression_T3 0.062 -0.108 0.000
## depression_T4 -0.007 0.041 0.102 0.000
##
## $mean
## depression_T1 depression_T2 depression_T3 depression_T4
## -0.006 0.071 -0.106 0.061# model 3 parameter estimates, global fit statistics,
# residuals
lavaan::summary(linear, fit.measures = TRUE, estimates = TRUE)## lavaan 0.6.17 ended normally after 55 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 12
##
## Used Total
## Number of observations 57 540
## Number of missing patterns 1
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 55.157 47.517
## Degrees of freedom 18 18
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.161
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 96.733 85.070
## Degrees of freedom 22 22
## P-value 0.000 0.000
## Scaling correction factor 1.137
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.503 0.532
## Tucker-Lewis Index (TLI) 0.392 0.428
##
## Robust Comparative Fit Index (CFI) 0.502
## Robust Tucker-Lewis Index (TLI) 0.391
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -120.456 -120.456
## Scaling correction factor 1.254
## for the MLR correction
## Loglikelihood unrestricted model (H1) -92.878 -92.878
## Scaling correction factor 1.198
## for the MLR correction
##
## Akaike (AIC) 264.912 264.912
## Bayesian (BIC) 289.429 289.429
## Sample-size adjusted Bayesian (SABIC) 251.706 251.706
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.190 0.170
## 90 Percent confidence interval - lower 0.134 0.116
## 90 Percent confidence interval - upper 0.249 0.225
## P-value H_0: RMSEA <= 0.050 0.000 0.001
## P-value H_0: RMSEA >= 0.080 0.999 0.995
##
## Robust RMSEA 0.183
## 90 Percent confidence interval - lower 0.120
## 90 Percent confidence interval - upper 0.247
## P-value H_0: Robust RMSEA <= 0.050 0.001
## P-value H_0: Robust RMSEA >= 0.080 0.995
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.180 0.180
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Intercept =~
## depression_T1 1.000
## depression_T2 1.000
## depression_T3 1.000
## depression_T4 1.000
## Linear =~
## depression_T1 0.000
## depression_T2 1.000
## depression_T3 2.000
## depression_T4 3.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## depression_T1 ~
## CASI_ADHD_CSum 0.066 0.022 2.951 0.003
## depression_T2 ~
## CASI_ADHD_CS_T 0.083 0.019 4.399 0.000
## depression_T3 ~
## CASI_ADHD_CS_T 0.066 0.013 5.035 0.000
## depression_T4 ~
## CASI_ADHDCS_T4 0.076 0.019 4.018 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## Intercept ~~
## Linear -0.069 0.018 -3.838 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## Intercept 0.635 0.285 2.226 0.026
## Linear -0.241 0.167 -1.439 0.150
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .depression_T1 0.000
## .depression_T2 0.154 0.028 5.463 0.000
## .depression_T3 0.126 0.032 3.943 0.000
## .depression_T4 0.226 0.052 4.307 0.000
## Intercept 0.178 0.049 3.626 0.000
## Linear 0.027 0.008 3.202 0.001lavaan::fitted(linear) ## $cov
## dpr_T1 dpr_T2 dpr_T3 dpr_T4 CASI_ADHD_CSm CASI_ADHD_CS_T2
## depression_T1 0.217
## depression_T2 0.127 0.276
## depression_T3 0.045 0.051 0.179
## depression_T4 -0.027 0.007 0.009 0.288
## CASI_ADHD_CSum 0.586 0.267 0.056 0.010 8.857
## CASI_ADHD_CSum_T2 0.214 0.656 0.298 0.278 3.229 7.917
## CASI_ADHD_CSum_T3 0.057 0.376 0.651 0.185 0.861 4.546
## CASI_ADHDCSum_T4 0.009 0.304 0.160 0.715 0.133 3.675
## CASI_ADHD_CS_T3 CASI_ADHDC
## depression_T1
## depression_T2
## depression_T3
## depression_T4
## CASI_ADHD_CSum
## CASI_ADHD_CSum_T2
## CASI_ADHD_CSum_T3 9.921
## CASI_ADHDCSum_T4 2.447 9.469
##
## $mean
## depression_T1 depression_T2 depression_T3 depression_T4
## 1.504 1.419 1.440 1.419
## CASI_ADHD_CSum CASI_ADHD_CSum_T2 CASI_ADHD_CSum_T3 CASI_ADHDCSum_T4
## 13.140 12.368 19.614 19.930lavaan::residuals(linear, type = "raw")## $type
## [1] "raw"
##
## $cov
## dpr_T1 dpr_T2 dpr_T3 dpr_T4 CASI_ADHD_CSm CASI_ADHD_CS_T2
## depression_T1 0.015
## depression_T2 -0.057 -0.113
## depression_T3 -0.043 -0.042 0.025
## depression_T4 0.091 0.041 0.059 -0.072
## CASI_ADHD_CSum 0.111 0.137 0.178 0.166 0.000
## CASI_ADHD_CSum_T2 -0.097 -0.250 0.231 0.089 0.000 0.000
## CASI_ADHD_CSum_T3 -0.086 -0.327 0.363 0.171 0.000 0.000
## CASI_ADHDCSum_T4 -0.096 0.068 0.102 -0.295 0.000 0.000
## CASI_ADHD_CS_T3 CASI_ADHDC
## depression_T1
## depression_T2
## depression_T3
## depression_T4
## CASI_ADHD_CSum
## CASI_ADHD_CSum_T2
## CASI_ADHD_CSum_T3 0.000
## CASI_ADHDCSum_T4 0.000 0.000
##
## $mean
## depression_T1 depression_T2 depression_T3 depression_T4
## 0.000 0.016 -0.019 0.015
## CASI_ADHD_CSum CASI_ADHD_CSum_T2 CASI_ADHD_CSum_T3 CASI_ADHDCSum_T4
## 0.000 0.000 0.000 0.000lavaan::residuals(linear, type = "standardized")## $type
## [1] "standardized"
##
## $cov
## dpr_T1 dpr_T2 dpr_T3 dpr_T4 CASI_ADHD_CSm CASI_ADHD_CS_T2
## depression_T1 0.278
## depression_T2 -0.988 -1.410
## depression_T3 -1.190 -1.501 1.103
## depression_T4 2.204 1.043 2.019 -1.158
## CASI_ADHD_CSum 1.002 0.878 1.466 0.804 0.000
## CASI_ADHD_CSum_T2 -0.325 -1.037 2.054 0.356 0.000 0.000
## CASI_ADHD_CSum_T3 -0.282 -1.433 3.118 0.705 0.000 0.000
## CASI_ADHDCSum_T4 -0.439 0.503 0.888 -2.000 0.000 0.000
## CASI_ADHD_CS_T3 CASI_ADHDC
## depression_T1
## depression_T2
## depression_T3
## depression_T4
## CASI_ADHD_CSum
## CASI_ADHD_CSum_T2
## CASI_ADHD_CSum_T3 0.000
## CASI_ADHDCSum_T4 0.000 0.000
##
## $mean
## depression_T1 depression_T2 depression_T3 depression_T4
## 0.000 0.988 -2.307 1.208
## CASI_ADHD_CSum CASI_ADHD_CSum_T2 CASI_ADHD_CSum_T3 CASI_ADHDCSum_T4
## 0.000 0.000 0.000 0.000lavaan::residuals(linear, type = "cor.bollen")## $type
## [1] "cor.bollen"
##
## $cov
## dpr_T1 dpr_T2 dpr_T3 dpr_T4 CASI_ADHD_CSm CASI_ADHD_CS_T2
## depression_T1 0.000
## depression_T2 -0.157 0.000
## depression_T3 -0.220 -0.178 0.000
## depression_T4 0.394 0.231 0.282 0.000
## CASI_ADHD_CSum 0.064 0.165 0.130 0.121 0.000
## CASI_ADHD_CSum_T2 -0.077 -0.086 0.166 0.096 0.000 0.000
## CASI_ADHD_CSum_T3 -0.058 -0.189 0.224 0.134 0.000 0.000
## CASI_ADHDCSum_T4 -0.065 0.111 0.065 -0.139 0.000 0.000
## CASI_ADHD_CS_T3 CASI_ADHDC
## depression_T1
## depression_T2
## depression_T3
## depression_T4
## CASI_ADHD_CSum
## CASI_ADHD_CSum_T2
## CASI_ADHD_CSum_T3 0.000
## CASI_ADHDCSum_T4 0.000 0.000
##
## $mean
## depression_T1 depression_T2 depression_T3 depression_T4
## 0.000 0.038 -0.042 0.033
## CASI_ADHD_CSum CASI_ADHD_CSum_T2 CASI_ADHD_CSum_T3 CASI_ADHDCSum_T4
## 0.000 0.000 0.000 0.000#standardized estimates
standardizedSolution(linear, type = "std.all", se= TRUE, pvalue = TRUE)## lhs op rhs est.std se z pvalue ci.lower
## 1 Intercept =~ depression_T1 0.906 0.064 14.109 0.000 0.780
## 2 Intercept =~ depression_T2 0.803 0.088 9.149 0.000 0.631
## 3 Intercept =~ depression_T3 0.996 0.133 7.462 0.000 0.734
## 4 Intercept =~ depression_T4 0.786 0.134 5.887 0.000 0.524
## 5 Linear =~ depression_T1 0.000 0.000 NA NA 0.000
## 6 Linear =~ depression_T2 0.312 0.053 5.828 0.000 0.207
## 7 Linear =~ depression_T3 0.774 0.160 4.827 0.000 0.460
## 8 Linear =~ depression_T4 0.916 0.130 7.027 0.000 0.661
## 9 depression_T1 ~~ depression_T1 0.000 0.000 NA NA 0.000
## 10 depression_T1 ~ CASI_ADHD_CSum 0.423 0.138 3.072 0.002 0.153
## 11 depression_T2 ~ CASI_ADHD_CSum_T2 0.443 0.092 4.821 0.000 0.263
## 12 depression_T3 ~ CASI_ADHD_CSum_T3 0.488 0.095 5.153 0.000 0.302
## 13 depression_T4 ~ CASI_ADHDCSum_T4 0.433 0.094 4.609 0.000 0.249
## 14 depression_T2 ~~ depression_T2 0.558 0.056 10.039 0.000 0.449
## 15 depression_T3 ~~ depression_T3 0.699 0.135 5.181 0.000 0.435
## 16 depression_T4 ~~ depression_T4 0.783 0.134 5.856 0.000 0.521
## 17 Intercept ~~ Intercept 1.000 0.000 NA NA 1.000
## 18 Linear ~~ Linear 1.000 0.000 NA NA 1.000
## 19 Intercept ~~ Linear -0.991 0.062 -15.994 0.000 -1.113
## 20 CASI_ADHD_CSum ~~ CASI_ADHD_CSum 1.000 0.000 NA NA 1.000
## 21 CASI_ADHD_CSum ~~ CASI_ADHD_CSum_T2 0.386 0.000 NA NA 0.386
## 22 CASI_ADHD_CSum ~~ CASI_ADHD_CSum_T3 0.092 0.000 NA NA 0.092
## 23 CASI_ADHD_CSum ~~ CASI_ADHDCSum_T4 0.014 0.000 NA NA 0.014
## 24 CASI_ADHD_CSum_T2 ~~ CASI_ADHD_CSum_T2 1.000 0.000 NA NA 1.000
## 25 CASI_ADHD_CSum_T2 ~~ CASI_ADHD_CSum_T3 0.513 0.000 NA NA 0.513
## 26 CASI_ADHD_CSum_T2 ~~ CASI_ADHDCSum_T4 0.424 0.000 NA NA 0.424
## 27 CASI_ADHD_CSum_T3 ~~ CASI_ADHD_CSum_T3 1.000 0.000 NA NA 1.000
## 28 CASI_ADHD_CSum_T3 ~~ CASI_ADHDCSum_T4 0.252 0.000 NA NA 0.252
## 29 CASI_ADHDCSum_T4 ~~ CASI_ADHDCSum_T4 1.000 0.000 NA NA 1.000
## 30 depression_T1 ~1 0.000 0.000 NA NA 0.000
## 31 depression_T2 ~1 0.000 0.000 NA NA 0.000
## 32 depression_T3 ~1 0.000 0.000 NA NA 0.000
## 33 depression_T4 ~1 0.000 0.000 NA NA 0.000
## 34 CASI_ADHD_CSum ~1 4.415 0.000 NA NA 4.415
## 35 CASI_ADHD_CSum_T2 ~1 4.396 0.000 NA NA 4.396
## 36 CASI_ADHD_CSum_T3 ~1 6.227 0.000 NA NA 6.227
## 37 CASI_ADHDCSum_T4 ~1 6.477 0.000 NA NA 6.477
## 38 Intercept ~1 1.506 0.657 2.292 0.022 0.218
## 39 Linear ~1 -1.470 1.079 -1.361 0.173 -3.585
## ci.upper
## 1 1.032
## 2 0.974
## 3 1.257
## 4 1.048
## 5 0.000
## 6 0.417
## 7 1.088
## 8 1.172
## 9 0.000
## 10 0.693
## 11 0.623
## 12 0.673
## 13 0.617
## 14 0.667
## 15 0.964
## 16 1.045
## 17 1.000
## 18 1.000
## 19 -0.870
## 20 1.000
## 21 0.386
## 22 0.092
## 23 0.014
## 24 1.000
## 25 0.513
## 26 0.424
## 27 1.000
## 28 0.252
## 29 1.000
## 30 0.000
## 31 0.000
## 32 0.000
## 33 0.000
## 34 4.415
## 35 4.396
## 36 6.227
## 37 6.477
## 38 2.794
## 39 0.646#unstandardized estimates
parameterEstimates(linear)## lhs op rhs est se z pvalue ci.lower
## 1 Intercept =~ depression_T1 1.000 0.000 NA NA 1.000
## 2 Intercept =~ depression_T2 1.000 0.000 NA NA 1.000
## 3 Intercept =~ depression_T3 1.000 0.000 NA NA 1.000
## 4 Intercept =~ depression_T4 1.000 0.000 NA NA 1.000
## 5 Linear =~ depression_T1 0.000 0.000 NA NA 0.000
## 6 Linear =~ depression_T2 1.000 0.000 NA NA 1.000
## 7 Linear =~ depression_T3 2.000 0.000 NA NA 2.000
## 8 Linear =~ depression_T4 3.000 0.000 NA NA 3.000
## 9 depression_T1 ~~ depression_T1 0.000 0.000 NA NA 0.000
## 10 depression_T1 ~ CASI_ADHD_CSum 0.066 0.022 2.951 0.003 0.022
## 11 depression_T2 ~ CASI_ADHD_CSum_T2 0.083 0.019 4.399 0.000 0.046
## 12 depression_T3 ~ CASI_ADHD_CSum_T3 0.066 0.013 5.035 0.000 0.040
## 13 depression_T4 ~ CASI_ADHDCSum_T4 0.076 0.019 4.018 0.000 0.039
## 14 depression_T2 ~~ depression_T2 0.154 0.028 5.463 0.000 0.099
## 15 depression_T3 ~~ depression_T3 0.126 0.032 3.943 0.000 0.063
## 16 depression_T4 ~~ depression_T4 0.226 0.052 4.307 0.000 0.123
## 17 Intercept ~~ Intercept 0.178 0.049 3.626 0.000 0.082
## 18 Linear ~~ Linear 0.027 0.008 3.202 0.001 0.010
## 19 Intercept ~~ Linear -0.069 0.018 -3.838 0.000 -0.104
## 20 CASI_ADHD_CSum ~~ CASI_ADHD_CSum 8.857 0.000 NA NA 8.857
## 21 CASI_ADHD_CSum ~~ CASI_ADHD_CSum_T2 3.229 0.000 NA NA 3.229
## 22 CASI_ADHD_CSum ~~ CASI_ADHD_CSum_T3 0.861 0.000 NA NA 0.861
## 23 CASI_ADHD_CSum ~~ CASI_ADHDCSum_T4 0.133 0.000 NA NA 0.133
## 24 CASI_ADHD_CSum_T2 ~~ CASI_ADHD_CSum_T2 7.917 0.000 NA NA 7.917
## 25 CASI_ADHD_CSum_T2 ~~ CASI_ADHD_CSum_T3 4.546 0.000 NA NA 4.546
## 26 CASI_ADHD_CSum_T2 ~~ CASI_ADHDCSum_T4 3.675 0.000 NA NA 3.675
## 27 CASI_ADHD_CSum_T3 ~~ CASI_ADHD_CSum_T3 9.921 0.000 NA NA 9.921
## 28 CASI_ADHD_CSum_T3 ~~ CASI_ADHDCSum_T4 2.447 0.000 NA NA 2.447
## 29 CASI_ADHDCSum_T4 ~~ CASI_ADHDCSum_T4 9.469 0.000 NA NA 9.469
## 30 depression_T1 ~1 0.000 0.000 NA NA 0.000
## 31 depression_T2 ~1 0.000 0.000 NA NA 0.000
## 32 depression_T3 ~1 0.000 0.000 NA NA 0.000
## 33 depression_T4 ~1 0.000 0.000 NA NA 0.000
## 34 CASI_ADHD_CSum ~1 13.140 0.000 NA NA 13.140
## 35 CASI_ADHD_CSum_T2 ~1 12.368 0.000 NA NA 12.368
## 36 CASI_ADHD_CSum_T3 ~1 19.614 0.000 NA NA 19.614
## 37 CASI_ADHDCSum_T4 ~1 19.930 0.000 NA NA 19.930
## 38 Intercept ~1 0.635 0.285 2.226 0.026 0.076
## 39 Linear ~1 -0.241 0.167 -1.439 0.150 -0.569
## ci.upper
## 1 1.000
## 2 1.000
## 3 1.000
## 4 1.000
## 5 0.000
## 6 1.000
## 7 2.000
## 8 3.000
## 9 0.000
## 10 0.110
## 11 0.120
## 12 0.091
## 13 0.112
## 14 0.210
## 15 0.188
## 16 0.328
## 17 0.274
## 18 0.043
## 19 -0.034
## 20 8.857
## 21 3.229
## 22 0.861
## 23 0.133
## 24 7.917
## 25 4.546
## 26 3.675
## 27 9.921
## 28 2.447
## 29 9.469
## 30 0.000
## 31 0.000
## 32 0.000
## 33 0.000
## 34 13.140
## 35 12.368
## 36 19.614
## 37 19.930
## 38 1.195
## 39 0.087#########################################################################################################
#with all socio-demographic covariates (continuous and categorical)
#########################################################################################################
predict.model <- '
# latent growth factors
Intercept =~ 1*depression_T1 + 1*depression_T2 + 1*depression_T3 + 1*depression_T4
Linear =~ 0*depression_T1 + 1*depression_T2 + 2*depression_T3 + 3*depression_T4
# covariates
Intercept ~ CASI_ADHD_CSum + CASI_ADHD_CSum_T2 + CASI_ADHD_CSum_T3 +
CASI_ADHDCSum_T4 + Academicperformance_total_T4 +
Familyincome_T1r + Educationhousehold + Child_gender + Childagecategory
Linear ~ CASI_ADHD_CSum + CASI_ADHD_CSum_T2 + CASI_ADHD_CSum_T3 +
CASI_ADHDCSum_T4 + Academicperformance_total_T4 +
Familyincome_T1r + Educationhousehold + Child_gender + Childagecategory
# observed variables regressed on the time-varying covariate
depression_T1 ~ CASI_ADHD_CSum
depression_T2 ~ CASI_ADHD_CSum_T2
depression_T3 ~ CASI_ADHD_CSum_T3
depression_T4 ~ CASI_ADHDCSum_T4'
# fit prediction model to data
predict <- lavaan::growth(predict.model, data=mydata,, missing = "fiml", estimator = "mlr")
lavaan::summary(predict, fit.measures = TRUE, rsquare = TRUE)## lavaan 0.6.17 ended normally after 112 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 31
##
## Used Total
## Number of observations 56 540
## Number of missing patterns 1
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 23.738 30.323
## Degrees of freedom 19 19
## P-value (Chi-square) 0.206 0.048
## Scaling correction factor 0.783
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 148.778 174.747
## Degrees of freedom 42 42
## P-value 0.000 0.000
## Scaling correction factor 0.851
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.956 0.915
## Tucker-Lewis Index (TLI) 0.902 0.811
##
## Robust Comparative Fit Index (CFI) 0.929
## Robust Tucker-Lewis Index (TLI) 0.843
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -70.958 -70.958
## Scaling correction factor 1.041
## for the MLR correction
## Loglikelihood unrestricted model (H1) -59.089 -59.089
## Scaling correction factor 0.943
## for the MLR correction
##
## Akaike (AIC) 203.916 203.916
## Bayesian (BIC) 266.702 266.702
## Sample-size adjusted Bayesian (SABIC) 169.270 169.270
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.067 0.103
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.142 0.178
## P-value H_0: RMSEA <= 0.050 0.347 0.136
## P-value H_0: RMSEA >= 0.080 0.436 0.719
##
## Robust RMSEA 0.086
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.148
## P-value H_0: Robust RMSEA <= 0.050 0.184
## P-value H_0: Robust RMSEA >= 0.080 0.595
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.050 0.050
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Intercept =~
## depression_T1 1.000
## depression_T2 1.000
## depression_T3 1.000
## depression_T4 1.000
## Linear =~
## depression_T1 0.000
## depression_T2 1.000
## depression_T3 2.000
## depression_T4 3.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## Intercept ~
## CASI_ADHD_CSum 0.008 0.023 0.346 0.729
## CASI_ADHD_CS_T -0.007 0.018 -0.381 0.704
## CASI_ADHD_CS_T 0.014 0.013 1.076 0.282
## CASI_ADHDCS_T4 0.012 0.013 0.929 0.353
## Acdmcprfrm__T4 -0.006 0.034 -0.186 0.853
## Familyincm_T1r -0.084 0.038 -2.200 0.028
## Educationhshld -0.135 0.071 -1.898 0.058
## Child_gender 0.147 0.100 1.470 0.142
## Childagecatgry -0.031 0.072 -0.427 0.669
## Linear ~
## CASI_ADHD_CSum -0.002 0.014 -0.123 0.902
## CASI_ADHD_CS_T 0.011 0.012 0.980 0.327
## CASI_ADHD_CS_T 0.015 0.008 1.740 0.082
## CASI_ADHDCS_T4 -0.009 0.010 -0.860 0.390
## Acdmcprfrm__T4 0.006 0.021 0.282 0.778
## Familyincm_T1r -0.000 0.024 -0.020 0.984
## Educationhshld 0.069 0.035 1.972 0.049
## Child_gender -0.024 0.063 -0.381 0.703
## Childagecatgry 0.019 0.046 0.426 0.670
## depression_T1 ~
## CASI_ADHD_CSum 0.057 0.030 1.905 0.057
## depression_T2 ~
## CASI_ADHD_CS_T 0.069 0.020 3.423 0.001
## depression_T3 ~
## CASI_ADHD_CS_T 0.050 0.016 3.141 0.002
## depression_T4 ~
## CASI_ADHDCS_T4 0.059 0.026 2.278 0.023
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## .Intercept ~~
## .Linear 0.010 0.012 0.827 0.408
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .Intercept 0.974 0.478 2.036 0.042
## .Linear -0.749 0.276 -2.717 0.007
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .depression_T1 0.166 0.056 2.972 0.003
## .depression_T2 0.099 0.017 5.973 0.000
## .depression_T3 0.107 0.027 3.920 0.000
## .depression_T4 0.159 0.045 3.537 0.000
## .Intercept -0.035 0.027 -1.262 0.207
## .Linear -0.004 0.007 -0.510 0.610
##
## R-Square:
## Estimate
## depression_T1 0.241
## depression_T2 0.383
## depression_T3 0.396
## depression_T4 0.304
## Intercept NA
## Linear NA#standardized estimates
standardizedSolution(predict, type = "std.all", se= TRUE, pvalue = TRUE)## lhs op rhs est.std se
## 1 Intercept =~ depression_T1 NA NA
## 2 Intercept =~ depression_T2 NA NA
## 3 Intercept =~ depression_T3 NA NA
## 4 Intercept =~ depression_T4 NA NA
## 5 Linear =~ depression_T1 0.000 0.000
## 6 Linear =~ depression_T2 0.060 0.401
## 7 Linear =~ depression_T3 0.113 0.763
## 8 Linear =~ depression_T4 0.149 1.005
## 9 Intercept ~ CASI_ADHD_CSum NA NA
## 10 Intercept ~ CASI_ADHD_CSum_T2 NA NA
## 11 Intercept ~ CASI_ADHD_CSum_T3 NA NA
## 12 Intercept ~ CASI_ADHDCSum_T4 NA NA
## 13 Intercept ~ Academicperformance_total_T4 NA NA
## 14 Intercept ~ Familyincome_T1r NA NA
## 15 Intercept ~ Educationhousehold NA NA
## 16 Intercept ~ Child_gender NA NA
## 17 Intercept ~ Childagecategory NA NA
## 18 Linear ~ CASI_ADHD_CSum -0.209 2.478
## 19 Linear ~ CASI_ADHD_CSum_T2 1.125 7.616
## 20 Linear ~ CASI_ADHD_CSum_T3 1.716 11.473
## 21 Linear ~ CASI_ADHDCSum_T4 -1.144 7.409
## 22 Linear ~ Academicperformance_total_T4 0.285 1.794
## 23 Linear ~ Familyincome_T1r -0.020 0.959
## 24 Linear ~ Educationhousehold 2.122 13.902
## 25 Linear ~ Child_gender -0.503 3.399
## 26 Linear ~ Childagecategory 0.391 2.553
## 27 depression_T1 ~ CASI_ADHD_CSum 0.363 0.178
## 28 depression_T2 ~ CASI_ADHD_CSum_T2 0.406 0.111
## 29 depression_T3 ~ CASI_ADHD_CSum_T3 0.333 0.105
## 30 depression_T4 ~ CASI_ADHDCSum_T4 0.380 0.164
## 31 depression_T1 ~~ depression_T1 0.759 0.165
## 32 depression_T2 ~~ depression_T2 0.617 0.094
## 33 depression_T3 ~~ depression_T3 0.604 0.118
## 34 depression_T4 ~~ depression_T4 0.696 0.146
## 35 Intercept ~~ Intercept NA NA
## 36 Linear ~~ Linear -6.245 95.789
## 37 Intercept ~~ Linear 0.927 0.444
## 38 CASI_ADHD_CSum ~~ CASI_ADHD_CSum 1.000 0.000
## 39 CASI_ADHD_CSum ~~ CASI_ADHD_CSum_T2 0.381 0.000
## 40 CASI_ADHD_CSum ~~ CASI_ADHD_CSum_T3 0.034 0.000
## 41 CASI_ADHD_CSum ~~ CASI_ADHDCSum_T4 -0.003 0.000
## 42 CASI_ADHD_CSum ~~ Academicperformance_total_T4 -0.169 0.000
## 43 CASI_ADHD_CSum ~~ Familyincome_T1r -0.335 0.000
## 44 CASI_ADHD_CSum ~~ Educationhousehold -0.269 0.000
## 45 CASI_ADHD_CSum ~~ Child_gender 0.282 0.000
## 46 CASI_ADHD_CSum ~~ Childagecategory 0.100 0.000
## 47 CASI_ADHD_CSum_T2 ~~ CASI_ADHD_CSum_T2 1.000 0.000
## 48 CASI_ADHD_CSum_T2 ~~ CASI_ADHD_CSum_T3 0.337 0.000
## 49 CASI_ADHD_CSum_T2 ~~ CASI_ADHDCSum_T4 0.425 0.000
## 50 CASI_ADHD_CSum_T2 ~~ Academicperformance_total_T4 0.098 0.000
## 51 CASI_ADHD_CSum_T2 ~~ Familyincome_T1r -0.286 0.000
## 52 CASI_ADHD_CSum_T2 ~~ Educationhousehold -0.331 0.000
## 53 CASI_ADHD_CSum_T2 ~~ Child_gender 0.083 0.000
## 54 CASI_ADHD_CSum_T2 ~~ Childagecategory 0.107 0.000
## 55 CASI_ADHD_CSum_T3 ~~ CASI_ADHD_CSum_T3 1.000 0.000
## 56 CASI_ADHD_CSum_T3 ~~ CASI_ADHDCSum_T4 0.217 0.000
## 57 CASI_ADHD_CSum_T3 ~~ Academicperformance_total_T4 0.015 0.000
## 58 CASI_ADHD_CSum_T3 ~~ Familyincome_T1r 0.073 0.000
## 59 CASI_ADHD_CSum_T3 ~~ Educationhousehold -0.235 0.000
## 60 CASI_ADHD_CSum_T3 ~~ Child_gender -0.237 0.000
## 61 CASI_ADHD_CSum_T3 ~~ Childagecategory 0.154 0.000
## 62 CASI_ADHDCSum_T4 ~~ CASI_ADHDCSum_T4 1.000 0.000
## 63 CASI_ADHDCSum_T4 ~~ Academicperformance_total_T4 0.123 0.000
## 64 CASI_ADHDCSum_T4 ~~ Familyincome_T1r -0.038 0.000
## 65 CASI_ADHDCSum_T4 ~~ Educationhousehold -0.216 0.000
## 66 CASI_ADHDCSum_T4 ~~ Child_gender -0.168 0.000
## 67 CASI_ADHDCSum_T4 ~~ Childagecategory 0.238 0.000
## 68 Academicperformance_total_T4 ~~ Academicperformance_total_T4 1.000 0.000
## 69 Academicperformance_total_T4 ~~ Familyincome_T1r 0.190 0.000
## 70 Academicperformance_total_T4 ~~ Educationhousehold -0.162 0.000
## 71 Academicperformance_total_T4 ~~ Child_gender -0.016 0.000
## 72 Academicperformance_total_T4 ~~ Childagecategory 0.006 0.000
## 73 Familyincome_T1r ~~ Familyincome_T1r 1.000 0.000
## 74 Familyincome_T1r ~~ Educationhousehold 0.243 0.000
## 75 Familyincome_T1r ~~ Child_gender -0.113 0.000
## 76 Familyincome_T1r ~~ Childagecategory -0.004 0.000
## 77 Educationhousehold ~~ Educationhousehold 1.000 0.000
## 78 Educationhousehold ~~ Child_gender 0.318 0.000
## 79 Educationhousehold ~~ Childagecategory 0.107 0.000
## 80 Child_gender ~~ Child_gender 1.000 0.000
## 81 Child_gender ~~ Childagecategory 0.037 0.000
## 82 Childagecategory ~~ Childagecategory 1.000 0.000
## 83 depression_T1 ~1 0.000 0.000
## 84 depression_T2 ~1 0.000 0.000
## 85 depression_T3 ~1 0.000 0.000
## 86 depression_T4 ~1 0.000 0.000
## 87 CASI_ADHD_CSum ~1 4.396 0.000
## 88 CASI_ADHD_CSum_T2 ~1 5.139 0.000
## 89 CASI_ADHD_CSum_T3 ~1 6.976 0.000
## 90 CASI_ADHDCSum_T4 ~1 6.460 0.000
## 91 Academicperformance_total_T4 ~1 2.644 0.000
## 92 Familyincome_T1r ~1 2.685 0.000
## 93 Educationhousehold ~1 6.238 0.000
## 94 Child_gender ~1 3.000 0.000
## 95 Childagecategory ~1 5.855 0.000
## 96 Intercept ~1 NA NA
## 97 Linear ~1 -31.420 207.191
## z pvalue ci.lower ci.upper
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA 0.000 0.000
## 6 0.149 0.882 -0.726 0.845
## 7 0.148 0.882 -1.383 1.610
## 8 0.149 0.882 -1.820 2.118
## 9 NA NA NA NA
## 10 NA NA NA NA
## 11 NA NA NA NA
## 12 NA NA NA NA
## 13 NA NA NA NA
## 14 NA NA NA NA
## 15 NA NA NA NA
## 16 NA NA NA NA
## 17 NA NA NA NA
## 18 -0.084 0.933 -5.066 4.648
## 19 0.148 0.883 -13.802 16.052
## 20 0.150 0.881 -20.771 24.203
## 21 -0.154 0.877 -15.665 13.377
## 22 0.159 0.874 -3.232 3.801
## 23 -0.021 0.984 -1.899 1.860
## 24 0.153 0.879 -25.126 29.371
## 25 -0.148 0.882 -7.165 6.158
## 26 0.153 0.878 -4.613 5.394
## 27 2.032 0.042 0.013 0.712
## 28 3.670 0.000 0.189 0.622
## 29 3.188 0.001 0.128 0.538
## 30 2.308 0.021 0.057 0.702
## 31 4.601 0.000 0.435 1.082
## 32 6.593 0.000 0.434 0.800
## 33 5.113 0.000 0.372 0.835
## 34 4.778 0.000 0.411 0.982
## 35 NA NA NA NA
## 36 -0.065 0.948 -193.989 181.498
## 37 2.087 0.037 0.056 1.798
## 38 NA NA 1.000 1.000
## 39 NA NA 0.381 0.381
## 40 NA NA 0.034 0.034
## 41 NA NA -0.003 -0.003
## 42 NA NA -0.169 -0.169
## 43 NA NA -0.335 -0.335
## 44 NA NA -0.269 -0.269
## 45 NA NA 0.282 0.282
## 46 NA NA 0.100 0.100
## 47 NA NA 1.000 1.000
## 48 NA NA 0.337 0.337
## 49 NA NA 0.425 0.425
## 50 NA NA 0.098 0.098
## 51 NA NA -0.286 -0.286
## 52 NA NA -0.331 -0.331
## 53 NA NA 0.083 0.083
## 54 NA NA 0.107 0.107
## 55 NA NA 1.000 1.000
## 56 NA NA 0.217 0.217
## 57 NA NA 0.015 0.015
## 58 NA NA 0.073 0.073
## 59 NA NA -0.235 -0.235
## 60 NA NA -0.237 -0.237
## 61 NA NA 0.154 0.154
## 62 NA NA 1.000 1.000
## 63 NA NA 0.123 0.123
## 64 NA NA -0.038 -0.038
## 65 NA NA -0.216 -0.216
## 66 NA NA -0.168 -0.168
## 67 NA NA 0.238 0.238
## 68 NA NA 1.000 1.000
## 69 NA NA 0.190 0.190
## 70 NA NA -0.162 -0.162
## 71 NA NA -0.016 -0.016
## 72 NA NA 0.006 0.006
## 73 NA NA 1.000 1.000
## 74 NA NA 0.243 0.243
## 75 NA NA -0.113 -0.113
## 76 NA NA -0.004 -0.004
## 77 NA NA 1.000 1.000
## 78 NA NA 0.318 0.318
## 79 NA NA 0.107 0.107
## 80 NA NA 1.000 1.000
## 81 NA NA 0.037 0.037
## 82 NA NA 1.000 1.000
## 83 NA NA 0.000 0.000
## 84 NA NA 0.000 0.000
## 85 NA NA 0.000 0.000
## 86 NA NA 0.000 0.000
## 87 NA NA 4.396 4.396
## 88 NA NA 5.139 5.139
## 89 NA NA 6.976 6.976
## 90 NA NA 6.460 6.460
## 91 NA NA 2.644 2.644
## 92 NA NA 2.685 2.685
## 93 NA NA 6.238 6.238
## 94 NA NA 3.000 3.000
## 95 NA NA 5.855 5.855
## 96 NA NA NA NA
## 97 -0.152 0.879 -437.507 374.667#unstandardized estimates
parameterEstimates(predict)## lhs op rhs est se
## 1 Intercept =~ depression_T1 1.000 0.000
## 2 Intercept =~ depression_T2 1.000 0.000
## 3 Intercept =~ depression_T3 1.000 0.000
## 4 Intercept =~ depression_T4 1.000 0.000
## 5 Linear =~ depression_T1 0.000 0.000
## 6 Linear =~ depression_T2 1.000 0.000
## 7 Linear =~ depression_T3 2.000 0.000
## 8 Linear =~ depression_T4 3.000 0.000
## 9 Intercept ~ CASI_ADHD_CSum 0.008 0.023
## 10 Intercept ~ CASI_ADHD_CSum_T2 -0.007 0.018
## 11 Intercept ~ CASI_ADHD_CSum_T3 0.014 0.013
## 12 Intercept ~ CASI_ADHDCSum_T4 0.012 0.013
## 13 Intercept ~ Academicperformance_total_T4 -0.006 0.034
## 14 Intercept ~ Familyincome_T1r -0.084 0.038
## 15 Intercept ~ Educationhousehold -0.135 0.071
## 16 Intercept ~ Child_gender 0.147 0.100
## 17 Intercept ~ Childagecategory -0.031 0.072
## 18 Linear ~ CASI_ADHD_CSum -0.002 0.014
## 19 Linear ~ CASI_ADHD_CSum_T2 0.011 0.012
## 20 Linear ~ CASI_ADHD_CSum_T3 0.015 0.008
## 21 Linear ~ CASI_ADHDCSum_T4 -0.009 0.010
## 22 Linear ~ Academicperformance_total_T4 0.006 0.021
## 23 Linear ~ Familyincome_T1r 0.000 0.024
## 24 Linear ~ Educationhousehold 0.069 0.035
## 25 Linear ~ Child_gender -0.024 0.063
## 26 Linear ~ Childagecategory 0.019 0.046
## 27 depression_T1 ~ CASI_ADHD_CSum 0.057 0.030
## 28 depression_T2 ~ CASI_ADHD_CSum_T2 0.069 0.020
## 29 depression_T3 ~ CASI_ADHD_CSum_T3 0.050 0.016
## 30 depression_T4 ~ CASI_ADHDCSum_T4 0.059 0.026
## 31 depression_T1 ~~ depression_T1 0.166 0.056
## 32 depression_T2 ~~ depression_T2 0.099 0.017
## 33 depression_T3 ~~ depression_T3 0.107 0.027
## 34 depression_T4 ~~ depression_T4 0.159 0.045
## 35 Intercept ~~ Intercept -0.035 0.027
## 36 Linear ~~ Linear -0.004 0.007
## 37 Intercept ~~ Linear 0.010 0.012
## 38 CASI_ADHD_CSum ~~ CASI_ADHD_CSum 8.867 0.000
## 39 CASI_ADHD_CSum ~~ CASI_ADHD_CSum_T2 2.682 0.000
## 40 CASI_ADHD_CSum ~~ CASI_ADHD_CSum_T3 0.285 0.000
## 41 CASI_ADHD_CSum ~~ CASI_ADHDCSum_T4 -0.025 0.000
## 42 CASI_ADHD_CSum ~~ Academicperformance_total_T4 -0.573 0.000
## 43 CASI_ADHD_CSum ~~ Familyincome_T1r -0.941 0.000
## 44 CASI_ADHD_CSum ~~ Educationhousehold -0.585 0.000
## 45 CASI_ADHD_CSum ~~ Child_gender 0.420 0.000
## 46 CASI_ADHD_CSum ~~ Childagecategory 0.143 0.000
## 47 CASI_ADHD_CSum_T2 ~~ CASI_ADHD_CSum_T2 5.599 0.000
## 48 CASI_ADHD_CSum_T2 ~~ CASI_ADHD_CSum_T3 2.220 0.000
## 49 CASI_ADHD_CSum_T2 ~~ CASI_ADHDCSum_T4 3.092 0.000
## 50 CASI_ADHD_CSum_T2 ~~ Academicperformance_total_T4 0.265 0.000
## 51 CASI_ADHD_CSum_T2 ~~ Familyincome_T1r -0.640 0.000
## 52 CASI_ADHD_CSum_T2 ~~ Educationhousehold -0.571 0.000
## 53 CASI_ADHD_CSum_T2 ~~ Child_gender 0.098 0.000
## 54 CASI_ADHD_CSum_T2 ~~ Childagecategory 0.121 0.000
## 55 CASI_ADHD_CSum_T3 ~~ CASI_ADHD_CSum_T3 7.742 0.000
## 56 CASI_ADHD_CSum_T3 ~~ CASI_ADHDCSum_T4 1.855 0.000
## 57 CASI_ADHD_CSum_T3 ~~ Academicperformance_total_T4 0.046 0.000
## 58 CASI_ADHD_CSum_T3 ~~ Familyincome_T1r 0.191 0.000
## 59 CASI_ADHD_CSum_T3 ~~ Educationhousehold -0.477 0.000
## 60 CASI_ADHD_CSum_T3 ~~ Child_gender -0.330 0.000
## 61 CASI_ADHD_CSum_T3 ~~ Childagecategory 0.206 0.000
## 62 CASI_ADHDCSum_T4 ~~ CASI_ADHDCSum_T4 9.467 0.000
## 63 CASI_ADHDCSum_T4 ~~ Academicperformance_total_T4 0.431 0.000
## 64 CASI_ADHDCSum_T4 ~~ Familyincome_T1r -0.112 0.000
## 65 CASI_ADHDCSum_T4 ~~ Educationhousehold -0.484 0.000
## 66 CASI_ADHDCSum_T4 ~~ Child_gender -0.259 0.000
## 67 CASI_ADHDCSum_T4 ~~ Childagecategory 0.350 0.000
## 68 Academicperformance_total_T4 ~~ Academicperformance_total_T4 1.303 0.000
## 69 Academicperformance_total_T4 ~~ Familyincome_T1r 0.205 0.000
## 70 Academicperformance_total_T4 ~~ Educationhousehold -0.135 0.000
## 71 Academicperformance_total_T4 ~~ Child_gender -0.009 0.000
## 72 Academicperformance_total_T4 ~~ Childagecategory 0.004 0.000
## 73 Familyincome_T1r ~~ Familyincome_T1r 0.892 0.000
## 74 Familyincome_T1r ~~ Educationhousehold 0.168 0.000
## 75 Familyincome_T1r ~~ Child_gender -0.054 0.000
## 76 Familyincome_T1r ~~ Childagecategory -0.002 0.000
## 77 Educationhousehold ~~ Educationhousehold 0.533 0.000
## 78 Educationhousehold ~~ Child_gender 0.116 0.000
## 79 Educationhousehold ~~ Childagecategory 0.037 0.000
## 80 Child_gender ~~ Child_gender 0.250 0.000
## 81 Child_gender ~~ Childagecategory 0.009 0.000
## 82 Childagecategory ~~ Childagecategory 0.229 0.000
## 83 depression_T1 ~1 0.000 0.000
## 84 depression_T2 ~1 0.000 0.000
## 85 depression_T3 ~1 0.000 0.000
## 86 depression_T4 ~1 0.000 0.000
## 87 CASI_ADHD_CSum ~1 13.089 0.000
## 88 CASI_ADHD_CSum_T2 ~1 12.161 0.000
## 89 CASI_ADHD_CSum_T3 ~1 19.411 0.000
## 90 CASI_ADHDCSum_T4 ~1 19.875 0.000
## 91 Academicperformance_total_T4 ~1 3.018 0.000
## 92 Familyincome_T1r ~1 2.536 0.000
## 93 Educationhousehold ~1 4.554 0.000
## 94 Child_gender ~1 1.500 0.000
## 95 Childagecategory ~1 2.804 0.000
## 96 Intercept ~1 0.974 0.478
## 97 Linear ~1 -0.749 0.276
## z pvalue ci.lower ci.upper
## 1 NA NA 1.000 1.000
## 2 NA NA 1.000 1.000
## 3 NA NA 1.000 1.000
## 4 NA NA 1.000 1.000
## 5 NA NA 0.000 0.000
## 6 NA NA 1.000 1.000
## 7 NA NA 2.000 2.000
## 8 NA NA 3.000 3.000
## 9 0.346 0.729 -0.037 0.053
## 10 -0.381 0.704 -0.042 0.029
## 11 1.076 0.282 -0.011 0.039
## 12 0.929 0.353 -0.013 0.036
## 13 -0.186 0.853 -0.072 0.060
## 14 -2.200 0.028 -0.159 -0.009
## 15 -1.898 0.058 -0.273 0.004
## 16 1.470 0.142 -0.049 0.344
## 17 -0.427 0.669 -0.172 0.111
## 18 -0.123 0.902 -0.028 0.025
## 19 0.980 0.327 -0.011 0.034
## 20 1.740 0.082 -0.002 0.031
## 21 -0.860 0.390 -0.029 0.011
## 22 0.282 0.778 -0.035 0.047
## 23 -0.020 0.984 -0.048 0.047
## 24 1.972 0.049 0.000 0.138
## 25 -0.381 0.703 -0.147 0.099
## 26 0.426 0.670 -0.070 0.109
## 27 1.905 0.057 -0.002 0.115
## 28 3.423 0.001 0.029 0.108
## 29 3.141 0.002 0.019 0.082
## 30 2.278 0.023 0.008 0.110
## 31 2.972 0.003 0.056 0.275
## 32 5.973 0.000 0.067 0.131
## 33 3.920 0.000 0.054 0.161
## 34 3.537 0.000 0.071 0.248
## 35 -1.262 0.207 -0.088 0.019
## 36 -0.510 0.610 -0.017 0.010
## 37 0.827 0.408 -0.014 0.035
## 38 NA NA 8.867 8.867
## 39 NA NA 2.682 2.682
## 40 NA NA 0.285 0.285
## 41 NA NA -0.025 -0.025
## 42 NA NA -0.573 -0.573
## 43 NA NA -0.941 -0.941
## 44 NA NA -0.585 -0.585
## 45 NA NA 0.420 0.420
## 46 NA NA 0.143 0.143
## 47 NA NA 5.599 5.599
## 48 NA NA 2.220 2.220
## 49 NA NA 3.092 3.092
## 50 NA NA 0.265 0.265
## 51 NA NA -0.640 -0.640
## 52 NA NA -0.571 -0.571
## 53 NA NA 0.098 0.098
## 54 NA NA 0.121 0.121
## 55 NA NA 7.742 7.742
## 56 NA NA 1.855 1.855
## 57 NA NA 0.046 0.046
## 58 NA NA 0.191 0.191
## 59 NA NA -0.477 -0.477
## 60 NA NA -0.330 -0.330
## 61 NA NA 0.206 0.206
## 62 NA NA 9.467 9.467
## 63 NA NA 0.431 0.431
## 64 NA NA -0.112 -0.112
## 65 NA NA -0.484 -0.484
## 66 NA NA -0.259 -0.259
## 67 NA NA 0.350 0.350
## 68 NA NA 1.303 1.303
## 69 NA NA 0.205 0.205
## 70 NA NA -0.135 -0.135
## 71 NA NA -0.009 -0.009
## 72 NA NA 0.004 0.004
## 73 NA NA 0.892 0.892
## 74 NA NA 0.168 0.168
## 75 NA NA -0.054 -0.054
## 76 NA NA -0.002 -0.002
## 77 NA NA 0.533 0.533
## 78 NA NA 0.116 0.116
## 79 NA NA 0.037 0.037
## 80 NA NA 0.250 0.250
## 81 NA NA 0.009 0.009
## 82 NA NA 0.229 0.229
## 83 NA NA 0.000 0.000
## 84 NA NA 0.000 0.000
## 85 NA NA 0.000 0.000
## 86 NA NA 0.000 0.000
## 87 NA NA 13.089 13.089
## 88 NA NA 12.161 12.161
## 89 NA NA 19.411 19.411
## 90 NA NA 19.875 19.875
## 91 NA NA 3.018 3.018
## 92 NA NA 2.536 2.536
## 93 NA NA 4.554 4.554
## 94 NA NA 1.500 1.500
## 95 NA NA 2.804 2.804
## 96 2.036 0.042 0.036 1.911
## 97 -2.717 0.007 -1.290 -0.209semPaths(predict,what = "path", whatLabels = "est", edge.label.cex=.7,
intercepts = TRUE, edge.color = "black", nCharNodes = 0, nCharEdges=0,
sizeLat = 6, sizeMan=9, exoVar = FALSE, exoCov = FALSE,
shapeInt = "circle", covAtResiduals = FALSE)
# predicted means and covariances for observed variables
lavaan::fitted(predict)## $cov
## dpr_T1 dpr_T2 dpr_T3 dpr_T4 CASI_ADHD_CSm
## depression_T1 0.219
## depression_T2 0.036 0.160
## depression_T3 0.023 0.047 0.177
## depression_T4 0.029 0.055 0.056 0.229
## CASI_ADHD_CSum 0.779 0.428 0.227 0.181 8.867
## CASI_ADHD_CSum_T2 0.342 0.600 0.354 0.451 2.682
## CASI_ADHD_CSum_T3 0.124 0.361 0.700 0.520 0.285
## CASI_ADHDCSum_T4 0.136 0.310 0.152 0.578 -0.025
## Academicperformance_total_T4 -0.042 0.008 -0.008 0.014 -0.573
## Familyincome_T1r -0.162 -0.141 -0.075 -0.080 -0.941
## Educationhousehold -0.115 -0.095 -0.054 -0.033 -0.585
## Child_gender 0.045 0.027 0.004 0.005 0.420
## Childagecategory 0.005 0.013 0.023 0.041 0.143
## CASI_ADHD_CS_T2 CASI_ADHD_CS_T3 CASI_ADHDC Ac__T4
## depression_T1
## depression_T2
## depression_T3
## depression_T4
## CASI_ADHD_CSum
## CASI_ADHD_CSum_T2 5.599
## CASI_ADHD_CSum_T3 2.220 7.742
## CASI_ADHDCSum_T4 3.092 1.855 9.467
## Academicperformance_total_T4 0.265 0.046 0.431 1.303
## Familyincome_T1r -0.640 0.191 -0.112 0.205
## Educationhousehold -0.571 -0.477 -0.484 -0.135
## Child_gender 0.098 -0.330 -0.259 -0.009
## Childagecategory 0.121 0.206 0.350 0.004
## Fml_T1 Edctnh Chld_g Chldgc
## depression_T1
## depression_T2
## depression_T3
## depression_T4
## CASI_ADHD_CSum
## CASI_ADHD_CSum_T2
## CASI_ADHD_CSum_T3
## CASI_ADHDCSum_T4
## Academicperformance_total_T4
## Familyincome_T1r 0.892
## Educationhousehold 0.168 0.533
## Child_gender -0.054 0.116 0.250
## Childagecategory -0.002 0.037 0.009 0.229
##
## $mean
## depression_T1 depression_T2
## 1.526 1.443
## depression_T3 depression_T4
## 1.414 1.436
## CASI_ADHD_CSum CASI_ADHD_CSum_T2
## 13.089 12.161
## CASI_ADHD_CSum_T3 CASI_ADHDCSum_T4
## 19.411 19.875
## Academicperformance_total_T4 Familyincome_T1r
## 3.018 2.536
## Educationhousehold Child_gender
## 4.554 1.500
## Childagecategory
## 2.804# predicted means for factors
lavaan::lavInspect(predict, add.labels = TRUE, "mean.lv")## Intercept Linear
## 0.781 -0.173lavaan::lavInspect(predict, add.labels = TRUE, "cor.lv")## Intrcp Linear
## Intercept 1
## Linear NaN 1# residuals
lavaan::residuals(predict, type = "raw")## $type
## [1] "raw"
##
## $cov
## dpr_T1 dpr_T2 dpr_T3 dpr_T4 CASI_ADHD_CSm
## depression_T1 -0.012
## depression_T2 0.020 -0.003
## depression_T3 -0.003 -0.027 0.018
## depression_T4 0.026 -0.012 0.019 -0.013
## CASI_ADHD_CSum -0.005 0.019 -0.031 0.021 0.000
## CASI_ADHD_CSum_T2 0.042 -0.042 0.009 0.014 0.000
## CASI_ADHD_CSum_T3 0.106 -0.169 0.160 -0.068 0.000
## CASI_ADHDCSum_T4 -0.155 0.107 0.068 -0.125 0.000
## Academicperformance_total_T4 -0.012 0.006 0.010 -0.013 0.000
## Familyincome_T1r 0.032 -0.049 0.045 -0.019 0.000
## Educationhousehold -0.002 0.004 -0.005 0.003 0.000
## Child_gender 0.000 0.017 -0.038 0.028 0.000
## Childagecategory -0.008 0.008 -0.001 -0.003 0.000
## CASI_ADHD_CS_T2 CASI_ADHD_CS_T3 CASI_ADHDC Ac__T4
## depression_T1
## depression_T2
## depression_T3
## depression_T4
## CASI_ADHD_CSum
## CASI_ADHD_CSum_T2 0.000
## CASI_ADHD_CSum_T3 0.000 0.000
## CASI_ADHDCSum_T4 0.000 0.000 0.000
## Academicperformance_total_T4 0.000 0.000 0.000 0.000
## Familyincome_T1r 0.000 0.000 0.000 0.000
## Educationhousehold 0.000 0.000 0.000 0.000
## Child_gender 0.000 0.000 0.000 0.000
## Childagecategory 0.000 0.000 0.000 0.000
## Fml_T1 Edctnh Chld_g Chldgc
## depression_T1
## depression_T2
## depression_T3
## depression_T4
## CASI_ADHD_CSum
## CASI_ADHD_CSum_T2
## CASI_ADHD_CSum_T3
## CASI_ADHDCSum_T4
## Academicperformance_total_T4
## Familyincome_T1r 0.000
## Educationhousehold 0.000 0.000
## Child_gender 0.000 0.000 0.000
## Childagecategory 0.000 0.000 0.000 0.000
##
## $mean
## depression_T1 depression_T2
## 0.000 0.003
## depression_T3 depression_T4
## -0.008 0.006
## CASI_ADHD_CSum CASI_ADHD_CSum_T2
## 0.000 0.000
## CASI_ADHD_CSum_T3 CASI_ADHDCSum_T4
## 0.000 0.000
## Academicperformance_total_T4 Familyincome_T1r
## 0.000 0.000
## Educationhousehold Child_gender
## 0.000 0.000
## Childagecategory
## 0.000lavaan::residuals(predict, type = "standardized.mplus")## $type
## [1] "standardized.mplus"
##
## $cov
## dpr_T1 dpr_T2 dpr_T3 dpr_T4 CASI_ADHD_CSm
## depression_T1 NA
## depression_T2 2.412 NA
## depression_T3 -0.179 NA 1.027
## depression_T4 2.383 -0.758 3.088 NA
## CASI_ADHD_CSum -0.099 0.347 -0.423 0.215 0.000
## CASI_ADHD_CSum_T2 0.689 NA NA 0.176 0.000
## CASI_ADHD_CSum_T3 1.353 -4.099 2.932 -0.859 0.000
## CASI_ADHDCSum_T4 -1.726 1.605 NA -2.093 0.000
## Academicperformance_total_T4 -0.319 0.212 0.374 -0.426 0.000
## Familyincome_T1r 1.182 -2.677 2.991 -0.542 0.000
## Educationhousehold -0.088 0.203 -0.362 0.146 0.000
## Child_gender 0.009 1.189 -2.436 2.435 0.000
## Childagecategory -1.119 0.570 -0.048 -0.231 0.000
## CASI_ADHD_CS_T2 CASI_ADHD_CS_T3 CASI_ADHDC Ac__T4
## depression_T1
## depression_T2
## depression_T3
## depression_T4
## CASI_ADHD_CSum
## CASI_ADHD_CSum_T2 0.000
## CASI_ADHD_CSum_T3 0.000 0.000
## CASI_ADHDCSum_T4 0.000 0.000 0.000
## Academicperformance_total_T4 0.000 0.000 0.000 0.000
## Familyincome_T1r 0.000 0.000 0.000 0.000
## Educationhousehold 0.000 0.000 0.000 0.000
## Child_gender 0.000 0.000 0.000 0.000
## Childagecategory 0.000 0.000 0.000 0.000
## Fml_T1 Edctnh Chld_g Chldgc
## depression_T1
## depression_T2
## depression_T3
## depression_T4
## CASI_ADHD_CSum
## CASI_ADHD_CSum_T2
## CASI_ADHD_CSum_T3
## CASI_ADHDCSum_T4
## Academicperformance_total_T4
## Familyincome_T1r 0.000
## Educationhousehold 0.000 0.000
## Child_gender 0.000 0.000 0.000
## Childagecategory 0.000 0.000 0.000 0.000
##
## $mean
## depression_T1 depression_T2
## NA NA
## depression_T3 depression_T4
## NA NA
## CASI_ADHD_CSum CASI_ADHD_CSum_T2
## 0 0
## CASI_ADHD_CSum_T3 CASI_ADHDCSum_T4
## 0 0
## Academicperformance_total_T4 Familyincome_T1r
## 0 0
## Educationhousehold Child_gender
## 0 0
## Childagecategory
## 0lavaan::residuals(predict, type = "cor.bollen")## $type
## [1] "cor.bollen"
##
## $cov
## dpr_T1 dpr_T2 dpr_T3 dpr_T4 CASI_ADHD_CSm
## depression_T1 0.000
## depression_T2 0.118 0.000
## depression_T3 -0.017 -0.167 0.000
## depression_T4 0.129 -0.053 0.085 0.000
## CASI_ADHD_CSum 0.012 0.019 -0.032 0.019 0.000
## CASI_ADHD_CSum_T2 0.048 -0.040 -0.008 0.024 0.000
## CASI_ADHD_CSum_T3 0.087 -0.150 0.101 -0.041 0.000
## CASI_ADHDCSum_T4 -0.108 0.090 0.045 -0.076 0.000
## Academicperformance_total_T4 -0.025 0.014 0.021 -0.024 0.000
## Familyincome_T1r 0.064 -0.135 0.118 -0.047 0.000
## Educationhousehold -0.016 0.013 -0.008 0.006 0.000
## Child_gender 0.006 0.088 -0.171 0.121 0.000
## Childagecategory -0.038 0.042 -0.010 -0.010 0.000
## CASI_ADHD_CS_T2 CASI_ADHD_CS_T3 CASI_ADHDC Ac__T4
## depression_T1
## depression_T2
## depression_T3
## depression_T4
## CASI_ADHD_CSum
## CASI_ADHD_CSum_T2 0.000
## CASI_ADHD_CSum_T3 0.000 0.000
## CASI_ADHDCSum_T4 0.000 0.000 0.000
## Academicperformance_total_T4 0.000 0.000 0.000 0.000
## Familyincome_T1r 0.000 0.000 0.000 0.000
## Educationhousehold 0.000 0.000 0.000 0.000
## Child_gender 0.000 0.000 0.000 0.000
## Childagecategory 0.000 0.000 0.000 0.000
## Fml_T1 Edctnh Chld_g Chldgc
## depression_T1
## depression_T2
## depression_T3
## depression_T4
## CASI_ADHD_CSum
## CASI_ADHD_CSum_T2
## CASI_ADHD_CSum_T3
## CASI_ADHDCSum_T4
## Academicperformance_total_T4
## Familyincome_T1r 0.000
## Educationhousehold 0.000 0.000
## Child_gender 0.000 0.000 0.000
## Childagecategory 0.000 0.000 0.000 0.000
##
## $mean
## depression_T1 depression_T2
## 0.001 0.009
## depression_T3 depression_T4
## -0.019 0.014
## CASI_ADHD_CSum CASI_ADHD_CSum_T2
## 0.000 0.000
## CASI_ADHD_CSum_T3 CASI_ADHDCSum_T4
## 0.000 0.000
## Academicperformance_total_T4 Familyincome_T1r
## 0.000 0.000
## Educationhousehold Child_gender
## 0.000 0.000
## Childagecategory
## 0.000#########################################################################################################
#########################################################################################################
#########################################################################################################
#########################################################################################################
# 3 step model fitting for anxiety #
#########################################################################################################
### MODEL 1 - no growth (intercept only) "random intercept-only model" - predicts no growth
# Loadings for all indicators on the intercept factor are fixed to equal 1.0.
# Intercept represents the factor mean, which is also the predicted average anxiety score
# over all years 1-4
noGrowth.model1 <- '
# specify intercept
# fix all loadings to 1.0
Intercept =~ 1*anxiety_T1 + 1*anxiety_T2 + 1*anxiety_T3 + 1*anxiety_T4
# fix error variance for r1 to zero
anxiety_T1 ~~ 0*anxiety_T1 '
#########################################################################################################
### MODEL 2 - latent basis growth model ("level and shape model")
# (curve fitting, level and shape)
# two growth factors: intercept (level) and shape
# loadings of all repeated measures of anxiety are fixed to equal 1.0, like in the
# random intercept-only model above. here, onnly looking at T1 to T4 change (overall shape)
# the basis model predicts growth trajectories that are not strictly linear - allows for
# linear or curvilinear change. the shape factor will represent the pattern
# this is called "non-linear curve fitting"
# unstandardized loadings for the shape factor are called basis coefficients
# two of these are fixed to equal constraints:
# one is specifying T1 as 0 defines the intercept [anxiety] factor mean)
# second is defining the random variation around the initial level
# the basis coefficients define the shape factor mean as the average change in anxiety
# between T1 and T4. it also scales the growth factor Shape so that a 1-unit change in
# time refers to the whole period of observation, or T1-T4 inclusive. so the loadings are
# interpreted as proportions of the total overall change that has occurred up to and
# including the corresponding measurement
# i.e., .70 for T3 would mean that .70, or 70% of the total increase in anxiety from T1-T4
# has occurred by T3
# the predicted average anxiety score is the sum of the initial mean at T1 plus the proportion
# of total change over T1-4 that has occurred up to and including the point of measurement
# the covariance represents the association between intercept and shape
# covariance indicates that higher initial levels of anxiety at T1 predict a higher rate of
# subsequent change between T1-4. so those who start at higher levels change the most over time
# a negative covariance indicates the opposite: Higher initial standing predicts less change over 4 timepoints
# a factor covariance of zero indicates that initial level has nothing to do with the rate of subsequent change
# maccallum-rmsea for model 2
# exact fit test
# power at N = 432
semTools::findRMSEApower(0, .05, 4, 432, .05, 1)## [1] 0.3437725# minimum N for power at least .80
semTools::findRMSEAsamplesize(0, .05, 4, .80, .05, 1)## [1] 1195basis.model1 <- '
Intercept =~ 1*anxiety_T1 + 1*anxiety_T2 + 1*anxiety_T3 + 1*anxiety_T4
# specify shape, first and last loadings fixed
Shape =~ 0*anxiety_T1 + anxiety_T2 + anxiety_T3 + 1*anxiety_T4
anxiety_T1 ~~ 0*anxiety_T1 '
#########################################################################################################
### MODEL 3 - linear growth model with only continuous covariates
# these assume change is strictly linear - essentially constrained versions of basis growth models
# but on the same variables. the coefficients are all fixed to equal constraints that correspond
# to times of measurement (none are free parameters)
# the mean for the intercept factor is the average anxiety score at T1, and its variance
# is the random variation around this mean initial level. The linear factor mean is the average amount
# of increase in anxiety per time point, which is assumed to be the same between all pairs of adjacent
# measurement occasions
linear.model1 <- '
Intercept =~ 1*anxiety_T1 + 1*anxiety_T2 + 1*anxiety_T3 + 1*anxiety_T4
# all loadings fixed to constants
Linear =~ 0*anxiety_T1 + 1*anxiety_T2 + 2*anxiety_T3 + 3*anxiety_T4
anxiety_T1 ~~ 0*anxiety_T1
# TVCs
anxiety_T1 ~ CASI_ADHD_CSum
anxiety_T2 ~ CASI_ADHD_CSum_T2
anxiety_T3 ~ CASI_ADHD_CSum_T3
anxiety_T4 ~ CASI_ADHDCSum_T4'
# fit model 1 to data
noGrowth1 <- lavaan::growth(noGrowth.model1, data=mydata, missing = "fiml", estimator = "mlr")
# fit model 2 to data
basis1 <- lavaan::growth(basis.model1, data=mydata, missing = "fiml", estimator = "mlr")
# fit model 3 to data
linear1 <- lavaan::growth(linear.model1, data=mydata, missing = "fiml", estimator = "mlr")
#########################################################################################################
# model chi-squares and chi-square difference tests
anova(noGrowth1, basis1)##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan NOTE:
## The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference
## test is a function of two standard (not robust) statistics.
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## basis1 4 1175.5 1216.9 11.656
## noGrowth1 9 1336.3 1357.0 182.411 136.43 5 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(basis1, linear1)##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan NOTE:
## The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference
## test is a function of two standard (not robust) statistics.
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## basis1 4 1175.52 1216.88 11.656
## linear1 18 127.72 152.23 80.843 65.835 14 1.085e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# model 1 parameter estimates, global fit statistics,
# residuals
lavaan::summary(noGrowth1, fit.measures = TRUE, estimates = TRUE)## lavaan 0.6.17 ended normally after 23 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 5
##
## Used Total
## Number of observations 462 540
## Number of missing patterns 14
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 182.410 162.677
## Degrees of freedom 9 9
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.121
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 122.337 99.669
## Degrees of freedom 6 6
## P-value 0.000 0.000
## Scaling correction factor 1.227
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.000 0.000
## Tucker-Lewis Index (TLI) 0.006 -0.094
##
## Robust Comparative Fit Index (CFI) 0.010
## Robust Tucker-Lewis Index (TLI) 0.340
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -663.139 -663.139
## Scaling correction factor 1.335
## for the MLR correction
## Loglikelihood unrestricted model (H1) -571.934 -571.934
## Scaling correction factor 1.198
## for the MLR correction
##
## Akaike (AIC) 1336.279 1336.279
## Bayesian (BIC) 1356.956 1356.956
## Sample-size adjusted Bayesian (SABIC) 1341.088 1341.088
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.204 0.192
## 90 Percent confidence interval - lower 0.179 0.168
## 90 Percent confidence interval - upper 0.231 0.217
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 1.000 1.000
##
## Robust RMSEA 0.452
## 90 Percent confidence interval - lower 0.382
## 90 Percent confidence interval - upper 0.525
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.702 0.702
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Intercept =~
## anxiety_T1 1.000
## anxiety_T2 1.000
## anxiety_T3 1.000
## anxiety_T4 1.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## Intercept 1.266 0.028 45.588 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .anxiety_T1 0.000
## .anxiety_T2 0.168 0.029 5.780 0.000
## .anxiety_T3 0.249 0.029 8.680 0.000
## .anxiety_T4 0.258 0.051 5.018 0.000
## Intercept 0.340 0.022 15.454 0.000lavaan::fitted(noGrowth1)## $cov
## anx_T1 anx_T2 anx_T3 anx_T4
## anxiety_T1 0.340
## anxiety_T2 0.340 0.508
## anxiety_T3 0.340 0.340 0.589
## anxiety_T4 0.340 0.340 0.340 0.598
##
## $mean
## anxiety_T1 anxiety_T2 anxiety_T3 anxiety_T4
## 1.266 1.266 1.266 1.266lavaan::residuals(noGrowth1, type = "standardized")## $type
## [1] "standardized"
##
## $cov
## anx_T1 anx_T2 anx_T3 anx_T4
## anxiety_T1 1.036
## anxiety_T2 -3.099 -2.154
## anxiety_T3 -2.995 -3.487 -2.441
## anxiety_T4 -2.468 -3.063 -2.930 -1.644
##
## $mean
## anxiety_T1 anxiety_T2 anxiety_T3 anxiety_T4
## -0.453 1.719 -1.586 1.451lavaan::residuals(noGrowth1, type = "cor.bollen")## $type
## [1] "cor.bollen"
##
## $cov
## anx_T1 anx_T2 anx_T3 anx_T4
## anxiety_T1 0.000
## anxiety_T2 -0.202 0.000
## anxiety_T3 -0.154 0.011 0.000
## anxiety_T4 -0.183 0.057 0.007 0.000
##
## $mean
## anxiety_T1 anxiety_T2 anxiety_T3 anxiety_T4
## -0.006 0.147 -0.107 0.133#standardized estimates
standardizedSolution(noGrowth1, type = "std.all", se= TRUE, pvalue = TRUE)## lhs op rhs est.std se z pvalue ci.lower ci.upper
## 1 Intercept =~ anxiety_T1 1.000 0.000 NA NA 1.000 1.000
## 2 Intercept =~ anxiety_T2 0.818 0.023 34.860 0 0.772 0.864
## 3 Intercept =~ anxiety_T3 0.760 0.021 36.774 0 0.720 0.801
## 4 Intercept =~ anxiety_T4 0.754 0.033 22.813 0 0.690 0.819
## 5 anxiety_T1 ~~ anxiety_T1 0.000 0.000 NA NA 0.000 0.000
## 6 anxiety_T2 ~~ anxiety_T2 0.331 0.038 8.609 0 0.255 0.406
## 7 anxiety_T3 ~~ anxiety_T3 0.422 0.031 13.440 0 0.361 0.484
## 8 anxiety_T4 ~~ anxiety_T4 0.431 0.050 8.641 0 0.333 0.529
## 9 Intercept ~~ Intercept 1.000 0.000 NA NA 1.000 1.000
## 10 anxiety_T1 ~1 0.000 0.000 NA NA 0.000 0.000
## 11 anxiety_T2 ~1 0.000 0.000 NA NA 0.000 0.000
## 12 anxiety_T3 ~1 0.000 0.000 NA NA 0.000 0.000
## 13 anxiety_T4 ~1 0.000 0.000 NA NA 0.000 0.000
## 14 Intercept ~1 2.170 0.092 23.667 0 1.990 2.350#unstandardized estimates
parameterEstimates(noGrowth1)## lhs op rhs est se z pvalue ci.lower ci.upper
## 1 Intercept =~ anxiety_T1 1.000 0.000 NA NA 1.000 1.000
## 2 Intercept =~ anxiety_T2 1.000 0.000 NA NA 1.000 1.000
## 3 Intercept =~ anxiety_T3 1.000 0.000 NA NA 1.000 1.000
## 4 Intercept =~ anxiety_T4 1.000 0.000 NA NA 1.000 1.000
## 5 anxiety_T1 ~~ anxiety_T1 0.000 0.000 NA NA 0.000 0.000
## 6 anxiety_T2 ~~ anxiety_T2 0.168 0.029 5.780 0 0.111 0.225
## 7 anxiety_T3 ~~ anxiety_T3 0.249 0.029 8.680 0 0.193 0.305
## 8 anxiety_T4 ~~ anxiety_T4 0.258 0.051 5.018 0 0.157 0.359
## 9 Intercept ~~ Intercept 0.340 0.022 15.454 0 0.297 0.384
## 10 anxiety_T1 ~1 0.000 0.000 NA NA 0.000 0.000
## 11 anxiety_T2 ~1 0.000 0.000 NA NA 0.000 0.000
## 12 anxiety_T3 ~1 0.000 0.000 NA NA 0.000 0.000
## 13 anxiety_T4 ~1 0.000 0.000 NA NA 0.000 0.000
## 14 Intercept ~1 1.266 0.028 45.588 0 1.212 1.320# model 2 parameter estimates, global fit statistics,
# residuals
# retained model
lavaan::summary(basis1, fit.measures = TRUE, rsquare = TRUE)## lavaan 0.6.17 ended normally after 35 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 10
##
## Used Total
## Number of observations 462 540
## Number of missing patterns 14
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 11.656 12.161
## Degrees of freedom 4 4
## P-value (Chi-square) 0.020 0.016
## Scaling correction factor 0.958
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 122.337 99.669
## Degrees of freedom 6 6
## P-value 0.000 0.000
## Scaling correction factor 1.227
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.934 0.913
## Tucker-Lewis Index (TLI) 0.901 0.869
##
## Robust Comparative Fit Index (CFI) 0.975
## Robust Tucker-Lewis Index (TLI) 0.963
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -577.762 -577.762
## Scaling correction factor 1.293
## for the MLR correction
## Loglikelihood unrestricted model (H1) -571.934 -571.934
## Scaling correction factor 1.198
## for the MLR correction
##
## Akaike (AIC) 1175.524 1175.524
## Bayesian (BIC) 1216.879 1216.879
## Sample-size adjusted Bayesian (SABIC) 1185.142 1185.142
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.064 0.066
## 90 Percent confidence interval - lower 0.023 0.025
## 90 Percent confidence interval - upper 0.109 0.112
## P-value H_0: RMSEA <= 0.050 0.240 0.219
## P-value H_0: RMSEA >= 0.080 0.320 0.353
##
## Robust RMSEA 0.107
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.241
## P-value H_0: Robust RMSEA <= 0.050 0.209
## P-value H_0: Robust RMSEA >= 0.080 0.697
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.059 0.059
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Intercept =~
## anxiety_T1 1.000
## anxiety_T2 1.000
## anxiety_T3 1.000
## anxiety_T4 1.000
## Shape =~
## anxiety_T1 0.000
## anxiety_T2 1.028 0.193 5.336 0.000
## anxiety_T3 0.983 0.212 4.634 0.000
## anxiety_T4 1.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## Intercept ~~
## Shape -0.183 0.036 -5.072 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## Intercept 1.265 0.030 42.167 0.000
## Shape 0.032 0.040 0.788 0.430
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .anxiety_T1 0.000
## .anxiety_T2 0.052 0.018 2.883 0.004
## .anxiety_T3 0.132 0.026 5.079 0.000
## .anxiety_T4 0.128 0.027 4.776 0.000
## Intercept 0.365 0.024 14.946 0.000
## Shape 0.158 0.053 2.951 0.003
##
## R-Square:
## Estimate
## anxiety_T1 1.000
## anxiety_T2 0.749
## anxiety_T3 0.543
## anxiety_T4 0.549lavaan::standardizedSolution(basis1)## lhs op rhs est.std se z pvalue ci.lower ci.upper
## 1 Intercept =~ anxiety_T1 1.000 0.000 NA NA 1.000 1.000
## 2 Intercept =~ anxiety_T2 1.328 0.098 13.603 0.000 1.137 1.520
## 3 Intercept =~ anxiety_T3 1.123 0.076 14.827 0.000 0.974 1.271
## 4 Intercept =~ anxiety_T4 1.133 0.064 17.686 0.000 1.007 1.258
## 5 Shape =~ anxiety_T1 0.000 0.000 NA NA 0.000 0.000
## 6 Shape =~ anxiety_T2 0.897 0.177 5.067 0.000 0.550 1.245
## 7 Shape =~ anxiety_T3 0.726 0.131 5.527 0.000 0.468 0.983
## 8 Shape =~ anxiety_T4 0.745 0.143 5.195 0.000 0.464 1.026
## 9 anxiety_T1 ~~ anxiety_T1 0.000 0.000 NA NA 0.000 0.000
## 10 anxiety_T2 ~~ anxiety_T2 0.251 0.081 3.115 0.002 0.093 0.409
## 11 anxiety_T3 ~~ anxiety_T3 0.457 0.069 6.632 0.000 0.322 0.592
## 12 anxiety_T4 ~~ anxiety_T4 0.451 0.080 5.613 0.000 0.293 0.608
## 13 Intercept ~~ Intercept 1.000 0.000 NA NA 1.000 1.000
## 14 Shape ~~ Shape 1.000 0.000 NA NA 1.000 1.000
## 15 Intercept ~~ Shape -0.764 0.054 -14.035 0.000 -0.870 -0.657
## 16 anxiety_T1 ~1 0.000 0.000 NA NA 0.000 0.000
## 17 anxiety_T2 ~1 0.000 0.000 NA NA 0.000 0.000
## 18 anxiety_T3 ~1 0.000 0.000 NA NA 0.000 0.000
## 19 anxiety_T4 ~1 0.000 0.000 NA NA 0.000 0.000
## 20 Intercept ~1 2.094 0.091 23.047 0.000 1.916 2.272
## 21 Shape ~1 0.079 0.095 0.833 0.405 -0.107 0.266#standardized estimates
standardizedSolution(basis1, type = "std.all", se= TRUE, pvalue = TRUE)## lhs op rhs est.std se z pvalue ci.lower ci.upper
## 1 Intercept =~ anxiety_T1 1.000 0.000 NA NA 1.000 1.000
## 2 Intercept =~ anxiety_T2 1.328 0.098 13.603 0.000 1.137 1.520
## 3 Intercept =~ anxiety_T3 1.123 0.076 14.827 0.000 0.974 1.271
## 4 Intercept =~ anxiety_T4 1.133 0.064 17.686 0.000 1.007 1.258
## 5 Shape =~ anxiety_T1 0.000 0.000 NA NA 0.000 0.000
## 6 Shape =~ anxiety_T2 0.897 0.177 5.067 0.000 0.550 1.245
## 7 Shape =~ anxiety_T3 0.726 0.131 5.527 0.000 0.468 0.983
## 8 Shape =~ anxiety_T4 0.745 0.143 5.195 0.000 0.464 1.026
## 9 anxiety_T1 ~~ anxiety_T1 0.000 0.000 NA NA 0.000 0.000
## 10 anxiety_T2 ~~ anxiety_T2 0.251 0.081 3.115 0.002 0.093 0.409
## 11 anxiety_T3 ~~ anxiety_T3 0.457 0.069 6.632 0.000 0.322 0.592
## 12 anxiety_T4 ~~ anxiety_T4 0.451 0.080 5.613 0.000 0.293 0.608
## 13 Intercept ~~ Intercept 1.000 0.000 NA NA 1.000 1.000
## 14 Shape ~~ Shape 1.000 0.000 NA NA 1.000 1.000
## 15 Intercept ~~ Shape -0.764 0.054 -14.035 0.000 -0.870 -0.657
## 16 anxiety_T1 ~1 0.000 0.000 NA NA 0.000 0.000
## 17 anxiety_T2 ~1 0.000 0.000 NA NA 0.000 0.000
## 18 anxiety_T3 ~1 0.000 0.000 NA NA 0.000 0.000
## 19 anxiety_T4 ~1 0.000 0.000 NA NA 0.000 0.000
## 20 Intercept ~1 2.094 0.091 23.047 0.000 1.916 2.272
## 21 Shape ~1 0.079 0.095 0.833 0.405 -0.107 0.266#unstandardized estimates
parameterEstimates(basis1)## lhs op rhs est se z pvalue ci.lower ci.upper
## 1 Intercept =~ anxiety_T1 1.000 0.000 NA NA 1.000 1.000
## 2 Intercept =~ anxiety_T2 1.000 0.000 NA NA 1.000 1.000
## 3 Intercept =~ anxiety_T3 1.000 0.000 NA NA 1.000 1.000
## 4 Intercept =~ anxiety_T4 1.000 0.000 NA NA 1.000 1.000
## 5 Shape =~ anxiety_T1 0.000 0.000 NA NA 0.000 0.000
## 6 Shape =~ anxiety_T2 1.028 0.193 5.336 0.000 0.650 1.405
## 7 Shape =~ anxiety_T3 0.983 0.212 4.634 0.000 0.567 1.399
## 8 Shape =~ anxiety_T4 1.000 0.000 NA NA 1.000 1.000
## 9 anxiety_T1 ~~ anxiety_T1 0.000 0.000 NA NA 0.000 0.000
## 10 anxiety_T2 ~~ anxiety_T2 0.052 0.018 2.883 0.004 0.017 0.087
## 11 anxiety_T3 ~~ anxiety_T3 0.132 0.026 5.079 0.000 0.081 0.183
## 12 anxiety_T4 ~~ anxiety_T4 0.128 0.027 4.776 0.000 0.076 0.181
## 13 Intercept ~~ Intercept 0.365 0.024 14.946 0.000 0.317 0.413
## 14 Shape ~~ Shape 0.158 0.053 2.951 0.003 0.053 0.263
## 15 Intercept ~~ Shape -0.183 0.036 -5.072 0.000 -0.254 -0.112
## 16 anxiety_T1 ~1 0.000 0.000 NA NA 0.000 0.000
## 17 anxiety_T2 ~1 0.000 0.000 NA NA 0.000 0.000
## 18 anxiety_T3 ~1 0.000 0.000 NA NA 0.000 0.000
## 19 anxiety_T4 ~1 0.000 0.000 NA NA 0.000 0.000
## 20 Intercept ~1 1.265 0.030 42.167 0.000 1.206 1.324
## 21 Shape ~1 0.032 0.040 0.788 0.430 -0.047 0.110semPaths(basis1,what = "path", whatLabels = "est", edge.label.cex=.7,
intercepts = TRUE, edge.color = "black", nCharNodes = 0, nCharEdges=0,
sizeLat = 6, sizeMan=9, exoVar = FALSE, exoCov = FALSE,
shapeInt = "circle", covAtResiduals = FALSE)
# implied covariances and means for observed variables
lavaan::fitted(basis1)## $cov
## anx_T1 anx_T2 anx_T3 anx_T4
## anxiety_T1 0.365
## anxiety_T2 0.177 0.207
## anxiety_T3 0.185 0.156 0.289
## anxiety_T4 0.182 0.155 0.157 0.284
##
## $mean
## anxiety_T1 anxiety_T2 anxiety_T3 anxiety_T4
## 1.265 1.297 1.296 1.296# implied means for latent growth factors
lavaan::lavInspect(basis1, add.labels = TRUE, "mean.lv")## Intercept Shape
## 1.265 0.032# residuals
lavaan::residuals(basis1, type = "raw")## $type
## [1] "raw"
##
## $cov
## anx_T1 anx_T2 anx_T3 anx_T4
## anxiety_T1 0.000
## anxiety_T2 -0.010 -0.008
## anxiety_T3 0.012 -0.004 -0.002
## anxiety_T4 0.007 0.009 0.013 0.013
##
## $mean
## anxiety_T1 anxiety_T2 anxiety_T3 anxiety_T4
## -0.002 0.035 -0.087 0.042lavaan::residuals(basis1, type = "standardized")## $type
## [1] "standardized"
##
## $cov
## anx_T1 anx_T2 anx_T3 anx_T4
## anxiety_T1 0.181
## anxiety_T2 -1.173 -0.579
## anxiety_T3 1.114 -0.341 -0.124
## anxiety_T4 0.411 0.541 0.585 0.546
##
## $mean
## anxiety_T1 anxiety_T2 anxiety_T3 anxiety_T4
## -0.920 1.666 -3.091 1.133lavaan::residuals(basis1, type = "cor.bollen")## $type
## [1] "cor.bollen"
##
## $cov
## anx_T1 anx_T2 anx_T3 anx_T4
## anxiety_T1 0.000
## anxiety_T2 -0.027 0.000
## anxiety_T3 0.037 -0.004 0.000
## anxiety_T4 0.007 0.033 0.034 0.000
##
## $mean
## anxiety_T1 anxiety_T2 anxiety_T3 anxiety_T4
## -0.004 0.078 -0.162 0.077# model 3 parameter estimates, global fit statistics,
# residuals
lavaan::summary(linear1, fit.measures = TRUE, estimates = TRUE)## lavaan 0.6.17 ended normally after 64 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 12
##
## Used Total
## Number of observations 57 540
## Number of missing patterns 1
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 80.843 78.461
## Degrees of freedom 18 18
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.030
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 139.834 137.466
## Degrees of freedom 22 22
## P-value 0.000 0.000
## Scaling correction factor 1.017
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.467 0.476
## Tucker-Lewis Index (TLI) 0.348 0.360
##
## Robust Comparative Fit Index (CFI) 0.468
## Robust Tucker-Lewis Index (TLI) 0.349
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -51.859 -51.859
## Scaling correction factor 1.319
## for the MLR correction
## Loglikelihood unrestricted model (H1) -11.437 -11.437
## Scaling correction factor 1.146
## for the MLR correction
##
## Akaike (AIC) 127.717 127.717
## Bayesian (BIC) 152.234 152.234
## Sample-size adjusted Bayesian (SABIC) 114.511 114.511
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.247 0.243
## 90 Percent confidence interval - lower 0.194 0.190
## 90 Percent confidence interval - upper 0.304 0.298
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 1.000 1.000
##
## Robust RMSEA 0.242
## 90 Percent confidence interval - lower 0.185
## 90 Percent confidence interval - upper 0.303
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.218 0.218
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Intercept =~
## anxiety_T1 1.000
## anxiety_T2 1.000
## anxiety_T3 1.000
## anxiety_T4 1.000
## Linear =~
## anxiety_T1 0.000
## anxiety_T2 1.000
## anxiety_T3 2.000
## anxiety_T4 3.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## anxiety_T1 ~
## CASI_ADHD_CSum 0.069 0.016 4.253 0.000
## anxiety_T2 ~
## CASI_ADHD_CS_T 0.081 0.015 5.250 0.000
## anxiety_T3 ~
## CASI_ADHD_CS_T 0.059 0.012 4.865 0.000
## anxiety_T4 ~
## CASI_ADHDCS_T4 0.074 0.016 4.607 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## Intercept ~~
## Linear -0.043 0.011 -3.822 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## Intercept 0.677 0.207 3.269 0.001
## Linear -0.214 0.119 -1.796 0.072
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .anxiety_T1 0.000
## .anxiety_T2 0.080 0.017 4.610 0.000
## .anxiety_T3 0.055 0.013 4.344 0.000
## .anxiety_T4 0.168 0.039 4.302 0.000
## Intercept 0.094 0.022 4.264 0.000
## Linear 0.020 0.006 3.265 0.001lavaan::fitted(linear1) ## $cov
## anx_T1 anx_T2 anx_T3 anx_T4 CASI_ADHD_CSm CASI_ADHD_CS_T2
## anxiety_T1 0.136
## anxiety_T2 0.069 0.160
## anxiety_T3 0.012 0.027 0.091
## anxiety_T4 -0.034 0.004 0.010 0.236
## CASI_ADHD_CSum 0.608 0.260 0.051 0.010 8.857
## CASI_ADHD_CSum_T2 0.222 0.638 0.268 0.273 3.229 7.917
## CASI_ADHD_CSum_T3 0.059 0.366 0.585 0.182 0.861 4.546
## CASI_ADHDCSum_T4 0.009 0.296 0.144 0.704 0.133 3.675
## CASI_ADHD_CS_T3 CASI_ADHDC
## anxiety_T1
## anxiety_T2
## anxiety_T3
## anxiety_T4
## CASI_ADHD_CSum
## CASI_ADHD_CSum_T2
## CASI_ADHD_CSum_T3 9.921
## CASI_ADHDCSum_T4 2.447 9.469
##
## $mean
## anxiety_T1 anxiety_T2 anxiety_T3 anxiety_T4
## 1.579 1.460 1.405 1.517
## CASI_ADHD_CSum CASI_ADHD_CSum_T2 CASI_ADHD_CSum_T3 CASI_ADHDCSum_T4
## 13.140 12.368 19.614 19.930lavaan::residuals(linear1, type = "raw")## $type
## [1] "raw"
##
## $cov
## anx_T1 anx_T2 anx_T3 anx_T4 CASI_ADHD_CSm CASI_ADHD_CS_T2
## anxiety_T1 -0.010
## anxiety_T2 -0.048 -0.057
## anxiety_T3 0.006 0.013 0.027
## anxiety_T4 0.071 0.052 0.013 -0.103
## CASI_ADHD_CSum -0.074 0.005 0.028 0.267 0.000
## CASI_ADHD_CSum_T2 0.057 0.002 0.146 0.203 0.000 0.000
## CASI_ADHD_CSum_T3 0.141 -0.135 0.237 0.039 0.000 0.000
## CASI_ADHDCSum_T4 0.092 0.114 0.139 -0.456 0.000 0.000
## CASI_ADHD_CS_T3 CASI_ADHDC
## anxiety_T1
## anxiety_T2
## anxiety_T3
## anxiety_T4
## CASI_ADHD_CSum
## CASI_ADHD_CSum_T2
## CASI_ADHD_CSum_T3 0.000
## CASI_ADHDCSum_T4 0.000 0.000
##
## $mean
## anxiety_T1 anxiety_T2 anxiety_T3 anxiety_T4
## 0.000 0.003 -0.011 0.021
## CASI_ADHD_CSum CASI_ADHD_CSum_T2 CASI_ADHD_CSum_T3 CASI_ADHDCSum_T4
## 0.000 0.000 0.000 0.000lavaan::residuals(linear1, type = "standardized")## $type
## [1] "standardized"
##
## $cov
## anx_T1 anx_T2 anx_T3 anx_T4 CASI_ADHD_CSm CASI_ADHD_CS_T2
## anxiety_T1 -0.301
## anxiety_T2 -1.408 -1.052
## anxiety_T3 0.464 1.187 3.771
## anxiety_T4 2.248 2.445 0.541 -1.402
## CASI_ADHD_CSum -1.045 0.065 0.292 1.921 0.000
## CASI_ADHD_CSum_T2 0.431 0.021 1.458 1.235 0.000 0.000
## CASI_ADHD_CSum_T3 0.993 -1.514 2.951 0.223 0.000 0.000
## CASI_ADHDCSum_T4 0.630 1.430 1.242 -3.063 0.000 0.000
## CASI_ADHD_CS_T3 CASI_ADHDC
## anxiety_T1
## anxiety_T2
## anxiety_T3
## anxiety_T4
## CASI_ADHD_CSum
## CASI_ADHD_CSum_T2
## CASI_ADHD_CSum_T3 0.000
## CASI_ADHDCSum_T4 0.000 0.000
##
## $mean
## anxiety_T1 anxiety_T2 anxiety_T3 anxiety_T4
## 0.000 0.188 -2.689 2.026
## CASI_ADHD_CSum CASI_ADHD_CSum_T2 CASI_ADHD_CSum_T3 CASI_ADHDCSum_T4
## 0.000 0.000 0.000 0.000lavaan::residuals(linear1, type = "cor.bollen")## $type
## [1] "cor.bollen"
##
## $cov
## anx_T1 anx_T2 anx_T3 anx_T4 CASI_ADHD_CSm CASI_ADHD_CS_T2
## anxiety_T1 0.000
## anxiety_T2 -0.280 0.000
## anxiety_T3 0.038 0.140 0.000
## anxiety_T4 0.478 0.461 0.114 0.000
## CASI_ADHD_CSum -0.048 0.059 0.020 0.249 0.000
## CASI_ADHD_CSum_T2 0.066 0.142 0.112 0.264 0.000 0.000
## CASI_ADHD_CSum_T3 0.128 -0.062 0.144 0.074 0.000 0.000
## CASI_ADHDCSum_T4 0.085 0.175 0.112 -0.250 0.000 0.000
## CASI_ADHD_CS_T3 CASI_ADHDC
## anxiety_T1
## anxiety_T2
## anxiety_T3
## anxiety_T4
## CASI_ADHD_CSum
## CASI_ADHD_CSum_T2
## CASI_ADHD_CSum_T3 0.000
## CASI_ADHDCSum_T4 0.000 0.000
##
## $mean
## anxiety_T1 anxiety_T2 anxiety_T3 anxiety_T4
## 0.000 0.008 -0.033 0.058
## CASI_ADHD_CSum CASI_ADHD_CSum_T2 CASI_ADHD_CSum_T3 CASI_ADHDCSum_T4
## 0.000 0.000 0.000 0.000#standardized estimates
standardizedSolution(linear1, type = "std.all", se= TRUE, pvalue = TRUE)## lhs op rhs est.std se z pvalue ci.lower
## 1 Intercept =~ anxiety_T1 0.833 0.070 11.919 0.000 0.696
## 2 Intercept =~ anxiety_T2 0.768 0.074 10.346 0.000 0.623
## 3 Intercept =~ anxiety_T3 1.015 0.138 7.382 0.000 0.746
## 4 Intercept =~ anxiety_T4 0.632 0.074 8.517 0.000 0.487
## 5 Linear =~ anxiety_T1 0.000 0.000 NA NA 0.000
## 6 Linear =~ anxiety_T2 0.353 0.057 6.247 0.000 0.242
## 7 Linear =~ anxiety_T3 0.933 0.167 5.590 0.000 0.606
## 8 Linear =~ anxiety_T4 0.872 0.094 9.244 0.000 0.687
## 9 anxiety_T1 ~~ anxiety_T1 0.000 0.000 NA NA 0.000
## 10 anxiety_T1 ~ CASI_ADHD_CSum 0.554 0.105 5.278 0.000 0.348
## 11 anxiety_T2 ~ CASI_ADHD_CSum_T2 0.568 0.088 6.417 0.000 0.394
## 12 anxiety_T3 ~ CASI_ADHD_CSum_T3 0.614 0.094 6.564 0.000 0.431
## 13 anxiety_T4 ~ CASI_ADHDCSum_T4 0.471 0.076 6.199 0.000 0.322
## 14 anxiety_T2 ~~ anxiety_T2 0.501 0.083 6.041 0.000 0.338
## 15 anxiety_T3 ~~ anxiety_T3 0.601 0.121 4.973 0.000 0.364
## 16 anxiety_T4 ~~ anxiety_T4 0.712 0.095 7.514 0.000 0.526
## 17 Intercept ~~ Intercept 1.000 0.000 NA NA 1.000
## 18 Linear ~~ Linear 1.000 0.000 NA NA 1.000
## 19 Intercept ~~ Linear -0.992 0.043 -22.894 0.000 -1.077
## 20 CASI_ADHD_CSum ~~ CASI_ADHD_CSum 1.000 0.000 NA NA 1.000
## 21 CASI_ADHD_CSum ~~ CASI_ADHD_CSum_T2 0.386 0.000 NA NA 0.386
## 22 CASI_ADHD_CSum ~~ CASI_ADHD_CSum_T3 0.092 0.000 NA NA 0.092
## 23 CASI_ADHD_CSum ~~ CASI_ADHDCSum_T4 0.014 0.000 NA NA 0.014
## 24 CASI_ADHD_CSum_T2 ~~ CASI_ADHD_CSum_T2 1.000 0.000 NA NA 1.000
## 25 CASI_ADHD_CSum_T2 ~~ CASI_ADHD_CSum_T3 0.513 0.000 NA NA 0.513
## 26 CASI_ADHD_CSum_T2 ~~ CASI_ADHDCSum_T4 0.424 0.000 NA NA 0.424
## 27 CASI_ADHD_CSum_T3 ~~ CASI_ADHD_CSum_T3 1.000 0.000 NA NA 1.000
## 28 CASI_ADHD_CSum_T3 ~~ CASI_ADHDCSum_T4 0.252 0.000 NA NA 0.252
## 29 CASI_ADHDCSum_T4 ~~ CASI_ADHDCSum_T4 1.000 0.000 NA NA 1.000
## 30 anxiety_T1 ~1 0.000 0.000 NA NA 0.000
## 31 anxiety_T2 ~1 0.000 0.000 NA NA 0.000
## 32 anxiety_T3 ~1 0.000 0.000 NA NA 0.000
## 33 anxiety_T4 ~1 0.000 0.000 NA NA 0.000
## 34 CASI_ADHD_CSum ~1 4.415 0.000 NA NA 4.415
## 35 CASI_ADHD_CSum_T2 ~1 4.396 0.000 NA NA 4.396
## 36 CASI_ADHD_CSum_T3 ~1 6.227 0.000 NA NA 6.227
## 37 CASI_ADHDCSum_T4 ~1 6.477 0.000 NA NA 6.477
## 38 Intercept ~1 2.206 0.695 3.176 0.001 0.845
## 39 Linear ~1 -1.518 0.751 -2.023 0.043 -2.990
## ci.upper
## 1 0.969
## 2 0.914
## 3 1.285
## 4 0.778
## 5 0.000
## 6 0.464
## 7 1.260
## 8 1.056
## 9 0.000
## 10 0.760
## 11 0.741
## 12 0.797
## 13 0.620
## 14 0.663
## 15 0.838
## 16 0.898
## 17 1.000
## 18 1.000
## 19 -0.907
## 20 1.000
## 21 0.386
## 22 0.092
## 23 0.014
## 24 1.000
## 25 0.513
## 26 0.424
## 27 1.000
## 28 0.252
## 29 1.000
## 30 0.000
## 31 0.000
## 32 0.000
## 33 0.000
## 34 4.415
## 35 4.396
## 36 6.227
## 37 6.477
## 38 3.567
## 39 -0.047#unstandardized estimates
parameterEstimates(linear1)## lhs op rhs est se z pvalue ci.lower
## 1 Intercept =~ anxiety_T1 1.000 0.000 NA NA 1.000
## 2 Intercept =~ anxiety_T2 1.000 0.000 NA NA 1.000
## 3 Intercept =~ anxiety_T3 1.000 0.000 NA NA 1.000
## 4 Intercept =~ anxiety_T4 1.000 0.000 NA NA 1.000
## 5 Linear =~ anxiety_T1 0.000 0.000 NA NA 0.000
## 6 Linear =~ anxiety_T2 1.000 0.000 NA NA 1.000
## 7 Linear =~ anxiety_T3 2.000 0.000 NA NA 2.000
## 8 Linear =~ anxiety_T4 3.000 0.000 NA NA 3.000
## 9 anxiety_T1 ~~ anxiety_T1 0.000 0.000 NA NA 0.000
## 10 anxiety_T1 ~ CASI_ADHD_CSum 0.069 0.016 4.253 0.000 0.037
## 11 anxiety_T2 ~ CASI_ADHD_CSum_T2 0.081 0.015 5.250 0.000 0.051
## 12 anxiety_T3 ~ CASI_ADHD_CSum_T3 0.059 0.012 4.865 0.000 0.035
## 13 anxiety_T4 ~ CASI_ADHDCSum_T4 0.074 0.016 4.607 0.000 0.043
## 14 anxiety_T2 ~~ anxiety_T2 0.080 0.017 4.610 0.000 0.046
## 15 anxiety_T3 ~~ anxiety_T3 0.055 0.013 4.344 0.000 0.030
## 16 anxiety_T4 ~~ anxiety_T4 0.168 0.039 4.302 0.000 0.091
## 17 Intercept ~~ Intercept 0.094 0.022 4.264 0.000 0.051
## 18 Linear ~~ Linear 0.020 0.006 3.265 0.001 0.008
## 19 Intercept ~~ Linear -0.043 0.011 -3.822 0.000 -0.065
## 20 CASI_ADHD_CSum ~~ CASI_ADHD_CSum 8.857 0.000 NA NA 8.857
## 21 CASI_ADHD_CSum ~~ CASI_ADHD_CSum_T2 3.229 0.000 NA NA 3.229
## 22 CASI_ADHD_CSum ~~ CASI_ADHD_CSum_T3 0.861 0.000 NA NA 0.861
## 23 CASI_ADHD_CSum ~~ CASI_ADHDCSum_T4 0.133 0.000 NA NA 0.133
## 24 CASI_ADHD_CSum_T2 ~~ CASI_ADHD_CSum_T2 7.917 0.000 NA NA 7.917
## 25 CASI_ADHD_CSum_T2 ~~ CASI_ADHD_CSum_T3 4.546 0.000 NA NA 4.546
## 26 CASI_ADHD_CSum_T2 ~~ CASI_ADHDCSum_T4 3.675 0.000 NA NA 3.675
## 27 CASI_ADHD_CSum_T3 ~~ CASI_ADHD_CSum_T3 9.921 0.000 NA NA 9.921
## 28 CASI_ADHD_CSum_T3 ~~ CASI_ADHDCSum_T4 2.447 0.000 NA NA 2.447
## 29 CASI_ADHDCSum_T4 ~~ CASI_ADHDCSum_T4 9.469 0.000 NA NA 9.469
## 30 anxiety_T1 ~1 0.000 0.000 NA NA 0.000
## 31 anxiety_T2 ~1 0.000 0.000 NA NA 0.000
## 32 anxiety_T3 ~1 0.000 0.000 NA NA 0.000
## 33 anxiety_T4 ~1 0.000 0.000 NA NA 0.000
## 34 CASI_ADHD_CSum ~1 13.140 0.000 NA NA 13.140
## 35 CASI_ADHD_CSum_T2 ~1 12.368 0.000 NA NA 12.368
## 36 CASI_ADHD_CSum_T3 ~1 19.614 0.000 NA NA 19.614
## 37 CASI_ADHDCSum_T4 ~1 19.930 0.000 NA NA 19.930
## 38 Intercept ~1 0.677 0.207 3.269 0.001 0.271
## 39 Linear ~1 -0.214 0.119 -1.796 0.072 -0.448
## ci.upper
## 1 1.000
## 2 1.000
## 3 1.000
## 4 1.000
## 5 0.000
## 6 1.000
## 7 2.000
## 8 3.000
## 9 0.000
## 10 0.100
## 11 0.111
## 12 0.083
## 13 0.106
## 14 0.114
## 15 0.080
## 16 0.244
## 17 0.138
## 18 0.032
## 19 -0.021
## 20 8.857
## 21 3.229
## 22 0.861
## 23 0.133
## 24 7.917
## 25 4.546
## 26 3.675
## 27 9.921
## 28 2.447
## 29 9.469
## 30 0.000
## 31 0.000
## 32 0.000
## 33 0.000
## 34 13.140
## 35 12.368
## 36 19.614
## 37 19.930
## 38 1.083
## 39 0.019#########################################################################################################
#with socio-demographic covariates
#########################################################################################################
predict.model3 <- '
# latent growth factors
Intercept =~ 1*anxiety_T1 + 1*anxiety_T2 + 1*anxiety_T3 + 1*anxiety_T4
Linear =~ 0*anxiety_T1 + 1*anxiety_T2 + 2*anxiety_T3 + 3*anxiety_T4
# covariates
Intercept ~ CASI_ADHD_CSum + CASI_ADHD_CSum_T2 + CASI_ADHD_CSum_T3 +
CASI_ADHDCSum_T4 + Academicperformance_total_T4 +
Familyincome_T1r + Educationhousehold + Child_gender + Childagecategory
Linear ~ CASI_ADHD_CSum + CASI_ADHD_CSum_T2 + CASI_ADHD_CSum_T3 +
CASI_ADHDCSum_T4 + Academicperformance_total_T4 +
Familyincome_T1r + Educationhousehold + Child_gender + Childagecategory
# observed variables regressed on the time-varying covariate
anxiety_T1 ~ CASI_ADHD_CSum
anxiety_T2 ~ CASI_ADHD_CSum_T2
anxiety_T3 ~ CASI_ADHD_CSum_T3
anxiety_T4 ~ CASI_ADHDCSum_T4'
# fit prediction model to data
predict3 <- lavaan::growth(predict.model3, data=mydata, missing = "fiml", estimator = "mlr")
lavaan::summary(predict3, fit.measures = TRUE, rsquare = TRUE)## lavaan 0.6.17 ended normally after 124 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 31
##
## Used Total
## Number of observations 56 540
## Number of missing patterns 1
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 38.273 42.037
## Degrees of freedom 19 19
## P-value (Chi-square) 0.005 0.002
## Scaling correction factor 0.910
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 195.098 223.533
## Degrees of freedom 42 42
## P-value 0.000 0.000
## Scaling correction factor 0.873
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.874 0.873
## Tucker-Lewis Index (TLI) 0.722 0.719
##
## Robust Comparative Fit Index (CFI) 0.863
## Robust Tucker-Lewis Index (TLI) 0.698
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1.770 -1.770
## Scaling correction factor 1.013
## for the MLR correction
## Loglikelihood unrestricted model (H1) 17.367 17.367
## Scaling correction factor 0.974
## for the MLR correction
##
## Akaike (AIC) 65.539 65.539
## Bayesian (BIC) 128.325 128.325
## Sample-size adjusted Bayesian (SABIC) 30.893 30.893
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.135 0.147
## 90 Percent confidence interval - lower 0.071 0.084
## 90 Percent confidence interval - upper 0.196 0.210
## P-value H_0: RMSEA <= 0.050 0.020 0.010
## P-value H_0: RMSEA >= 0.080 0.926 0.958
##
## Robust RMSEA 0.141
## 90 Percent confidence interval - lower 0.085
## 90 Percent confidence interval - upper 0.198
## P-value H_0: Robust RMSEA <= 0.050 0.007
## P-value H_0: Robust RMSEA >= 0.080 0.961
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.063 0.063
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Intercept =~
## anxiety_T1 1.000
## anxiety_T2 1.000
## anxiety_T3 1.000
## anxiety_T4 1.000
## Linear =~
## anxiety_T1 0.000
## anxiety_T2 1.000
## anxiety_T3 2.000
## anxiety_T4 3.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## Intercept ~
## CASI_ADHD_CSum -0.023 0.015 -1.519 0.129
## CASI_ADHD_CS_T -0.020 0.014 -1.388 0.165
## CASI_ADHD_CS_T 0.007 0.008 0.902 0.367
## CASI_ADHDCS_T4 0.025 0.009 2.937 0.003
## Acdmcprfrm__T4 0.042 0.024 1.725 0.085
## Familyincm_T1r -0.104 0.022 -4.677 0.000
## Educationhshld -0.023 0.048 -0.488 0.625
## Child_gender -0.028 0.063 -0.442 0.658
## Childagecatgry -0.050 0.045 -1.101 0.271
## Linear ~
## CASI_ADHD_CSum 0.009 0.007 1.209 0.226
## CASI_ADHD_CS_T 0.028 0.007 3.766 0.000
## CASI_ADHD_CS_T 0.005 0.005 0.986 0.324
## CASI_ADHDCS_T4 -0.019 0.007 -2.719 0.007
## Acdmcprfrm__T4 -0.026 0.014 -1.816 0.069
## Familyincm_T1r 0.037 0.014 2.715 0.007
## Educationhshld -0.006 0.026 -0.229 0.819
## Child_gender 0.031 0.037 0.851 0.395
## Childagecatgry 0.007 0.023 0.300 0.764
## anxiety_T1 ~
## CASI_ADHD_CSum 0.089 0.022 4.042 0.000
## anxiety_T2 ~
## CASI_ADHD_CS_T 0.086 0.016 5.281 0.000
## anxiety_T3 ~
## CASI_ADHD_CS_T 0.050 0.014 3.477 0.001
## anxiety_T4 ~
## CASI_ADHDCS_T4 0.056 0.022 2.580 0.010
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## .Intercept ~~
## .Linear 0.015 0.007 2.102 0.036
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .Intercept 0.736 0.355 2.071 0.038
## .Linear -0.230 0.182 -1.266 0.205
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .anxiety_T1 0.118 0.035 3.345 0.001
## .anxiety_T2 0.047 0.012 3.880 0.000
## .anxiety_T3 0.058 0.010 5.536 0.000
## .anxiety_T4 0.103 0.023 4.408 0.000
## .Intercept -0.029 0.014 -2.123 0.034
## .Linear -0.009 0.004 -2.130 0.033
##
## R-Square:
## Estimate
## anxiety_T1 0.188
## anxiety_T2 0.564
## anxiety_T3 0.444
## anxiety_T4 0.241
## Intercept NA
## Linear NA#standardized estimates
standardizedSolution(predict3, type = "std.all", se= TRUE, pvalue = TRUE)## lhs op rhs est.std se
## 1 Intercept =~ anxiety_T1 NA NA
## 2 Intercept =~ anxiety_T2 NA NA
## 3 Intercept =~ anxiety_T3 NA NA
## 4 Intercept =~ anxiety_T4 NA NA
## 5 Linear =~ anxiety_T1 NA NA
## 6 Linear =~ anxiety_T2 NA NA
## 7 Linear =~ anxiety_T3 NA NA
## 8 Linear =~ anxiety_T4 NA NA
## 9 Intercept ~ CASI_ADHD_CSum NA NA
## 10 Intercept ~ CASI_ADHD_CSum_T2 NA NA
## 11 Intercept ~ CASI_ADHD_CSum_T3 NA NA
## 12 Intercept ~ CASI_ADHDCSum_T4 NA NA
## 13 Intercept ~ Academicperformance_total_T4 NA NA
## 14 Intercept ~ Familyincome_T1r NA NA
## 15 Intercept ~ Educationhousehold NA NA
## 16 Intercept ~ Child_gender NA NA
## 17 Intercept ~ Childagecategory NA NA
## 18 Linear ~ CASI_ADHD_CSum NA NA
## 19 Linear ~ CASI_ADHD_CSum_T2 NA NA
## 20 Linear ~ CASI_ADHD_CSum_T3 NA NA
## 21 Linear ~ CASI_ADHDCSum_T4 NA NA
## 22 Linear ~ Academicperformance_total_T4 NA NA
## 23 Linear ~ Familyincome_T1r NA NA
## 24 Linear ~ Educationhousehold NA NA
## 25 Linear ~ Child_gender NA NA
## 26 Linear ~ Childagecategory NA NA
## 27 anxiety_T1 ~ CASI_ADHD_CSum 0.696 0.171
## 28 anxiety_T2 ~ CASI_ADHD_CSum_T2 0.617 0.107
## 29 anxiety_T3 ~ CASI_ADHD_CSum_T3 0.436 0.110
## 30 anxiety_T4 ~ CASI_ADHDCSum_T4 0.469 0.181
## 31 anxiety_T1 ~~ anxiety_T1 0.812 0.136
## 32 anxiety_T2 ~~ anxiety_T2 0.436 0.100
## 33 anxiety_T3 ~~ anxiety_T3 0.556 0.075
## 34 anxiety_T4 ~~ anxiety_T4 0.759 0.124
## 35 Intercept ~~ Intercept NA NA
## 36 Linear ~~ Linear NA NA
## 37 Intercept ~~ Linear 0.917 0.063
## 38 CASI_ADHD_CSum ~~ CASI_ADHD_CSum 1.000 0.000
## 39 CASI_ADHD_CSum ~~ CASI_ADHD_CSum_T2 0.381 0.000
## 40 CASI_ADHD_CSum ~~ CASI_ADHD_CSum_T3 0.034 0.000
## 41 CASI_ADHD_CSum ~~ CASI_ADHDCSum_T4 -0.003 0.000
## 42 CASI_ADHD_CSum ~~ Academicperformance_total_T4 -0.169 0.000
## 43 CASI_ADHD_CSum ~~ Familyincome_T1r -0.335 0.000
## 44 CASI_ADHD_CSum ~~ Educationhousehold -0.269 0.000
## 45 CASI_ADHD_CSum ~~ Child_gender 0.282 0.000
## 46 CASI_ADHD_CSum ~~ Childagecategory 0.100 0.000
## 47 CASI_ADHD_CSum_T2 ~~ CASI_ADHD_CSum_T2 1.000 0.000
## 48 CASI_ADHD_CSum_T2 ~~ CASI_ADHD_CSum_T3 0.337 0.000
## 49 CASI_ADHD_CSum_T2 ~~ CASI_ADHDCSum_T4 0.425 0.000
## 50 CASI_ADHD_CSum_T2 ~~ Academicperformance_total_T4 0.098 0.000
## 51 CASI_ADHD_CSum_T2 ~~ Familyincome_T1r -0.286 0.000
## 52 CASI_ADHD_CSum_T2 ~~ Educationhousehold -0.331 0.000
## 53 CASI_ADHD_CSum_T2 ~~ Child_gender 0.083 0.000
## 54 CASI_ADHD_CSum_T2 ~~ Childagecategory 0.107 0.000
## 55 CASI_ADHD_CSum_T3 ~~ CASI_ADHD_CSum_T3 1.000 0.000
## 56 CASI_ADHD_CSum_T3 ~~ CASI_ADHDCSum_T4 0.217 0.000
## 57 CASI_ADHD_CSum_T3 ~~ Academicperformance_total_T4 0.015 0.000
## 58 CASI_ADHD_CSum_T3 ~~ Familyincome_T1r 0.073 0.000
## 59 CASI_ADHD_CSum_T3 ~~ Educationhousehold -0.235 0.000
## 60 CASI_ADHD_CSum_T3 ~~ Child_gender -0.237 0.000
## 61 CASI_ADHD_CSum_T3 ~~ Childagecategory 0.154 0.000
## 62 CASI_ADHDCSum_T4 ~~ CASI_ADHDCSum_T4 1.000 0.000
## 63 CASI_ADHDCSum_T4 ~~ Academicperformance_total_T4 0.123 0.000
## 64 CASI_ADHDCSum_T4 ~~ Familyincome_T1r -0.038 0.000
## 65 CASI_ADHDCSum_T4 ~~ Educationhousehold -0.216 0.000
## 66 CASI_ADHDCSum_T4 ~~ Child_gender -0.168 0.000
## 67 CASI_ADHDCSum_T4 ~~ Childagecategory 0.238 0.000
## 68 Academicperformance_total_T4 ~~ Academicperformance_total_T4 1.000 0.000
## 69 Academicperformance_total_T4 ~~ Familyincome_T1r 0.190 0.000
## 70 Academicperformance_total_T4 ~~ Educationhousehold -0.162 0.000
## 71 Academicperformance_total_T4 ~~ Child_gender -0.016 0.000
## 72 Academicperformance_total_T4 ~~ Childagecategory 0.006 0.000
## 73 Familyincome_T1r ~~ Familyincome_T1r 1.000 0.000
## 74 Familyincome_T1r ~~ Educationhousehold 0.243 0.000
## 75 Familyincome_T1r ~~ Child_gender -0.113 0.000
## 76 Familyincome_T1r ~~ Childagecategory -0.004 0.000
## 77 Educationhousehold ~~ Educationhousehold 1.000 0.000
## 78 Educationhousehold ~~ Child_gender 0.318 0.000
## 79 Educationhousehold ~~ Childagecategory 0.107 0.000
## 80 Child_gender ~~ Child_gender 1.000 0.000
## 81 Child_gender ~~ Childagecategory 0.037 0.000
## 82 Childagecategory ~~ Childagecategory 1.000 0.000
## 83 anxiety_T1 ~1 0.000 0.000
## 84 anxiety_T2 ~1 0.000 0.000
## 85 anxiety_T3 ~1 0.000 0.000
## 86 anxiety_T4 ~1 0.000 0.000
## 87 CASI_ADHD_CSum ~1 4.396 0.000
## 88 CASI_ADHD_CSum_T2 ~1 5.139 0.000
## 89 CASI_ADHD_CSum_T3 ~1 6.976 0.000
## 90 CASI_ADHDCSum_T4 ~1 6.460 0.000
## 91 Academicperformance_total_T4 ~1 2.644 0.000
## 92 Familyincome_T1r ~1 2.685 0.000
## 93 Educationhousehold ~1 6.238 0.000
## 94 Child_gender ~1 3.000 0.000
## 95 Childagecategory ~1 5.855 0.000
## 96 Intercept ~1 NA NA
## 97 Linear ~1 NA NA
## z pvalue ci.lower ci.upper
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## 7 NA NA NA NA
## 8 NA NA NA NA
## 9 NA NA NA NA
## 10 NA NA NA NA
## 11 NA NA NA NA
## 12 NA NA NA NA
## 13 NA NA NA NA
## 14 NA NA NA NA
## 15 NA NA NA NA
## 16 NA NA NA NA
## 17 NA NA NA NA
## 18 NA NA NA NA
## 19 NA NA NA NA
## 20 NA NA NA NA
## 21 NA NA NA NA
## 22 NA NA NA NA
## 23 NA NA NA NA
## 24 NA NA NA NA
## 25 NA NA NA NA
## 26 NA NA NA NA
## 27 4.083 0.000 0.362 1.031
## 28 5.745 0.000 0.407 0.828
## 29 3.951 0.000 0.220 0.652
## 30 2.596 0.009 0.115 0.823
## 31 5.972 0.000 0.546 1.079
## 32 4.360 0.000 0.240 0.633
## 33 7.441 0.000 0.410 0.703
## 34 6.128 0.000 0.516 1.002
## 35 NA NA NA NA
## 36 NA NA NA NA
## 37 14.597 0.000 0.794 1.041
## 38 NA NA 1.000 1.000
## 39 NA NA 0.381 0.381
## 40 NA NA 0.034 0.034
## 41 NA NA -0.003 -0.003
## 42 NA NA -0.169 -0.169
## 43 NA NA -0.335 -0.335
## 44 NA NA -0.269 -0.269
## 45 NA NA 0.282 0.282
## 46 NA NA 0.100 0.100
## 47 NA NA 1.000 1.000
## 48 NA NA 0.337 0.337
## 49 NA NA 0.425 0.425
## 50 NA NA 0.098 0.098
## 51 NA NA -0.286 -0.286
## 52 NA NA -0.331 -0.331
## 53 NA NA 0.083 0.083
## 54 NA NA 0.107 0.107
## 55 NA NA 1.000 1.000
## 56 NA NA 0.217 0.217
## 57 NA NA 0.015 0.015
## 58 NA NA 0.073 0.073
## 59 NA NA -0.235 -0.235
## 60 NA NA -0.237 -0.237
## 61 NA NA 0.154 0.154
## 62 NA NA 1.000 1.000
## 63 NA NA 0.123 0.123
## 64 NA NA -0.038 -0.038
## 65 NA NA -0.216 -0.216
## 66 NA NA -0.168 -0.168
## 67 NA NA 0.238 0.238
## 68 NA NA 1.000 1.000
## 69 NA NA 0.190 0.190
## 70 NA NA -0.162 -0.162
## 71 NA NA -0.016 -0.016
## 72 NA NA 0.006 0.006
## 73 NA NA 1.000 1.000
## 74 NA NA 0.243 0.243
## 75 NA NA -0.113 -0.113
## 76 NA NA -0.004 -0.004
## 77 NA NA 1.000 1.000
## 78 NA NA 0.318 0.318
## 79 NA NA 0.107 0.107
## 80 NA NA 1.000 1.000
## 81 NA NA 0.037 0.037
## 82 NA NA 1.000 1.000
## 83 NA NA 0.000 0.000
## 84 NA NA 0.000 0.000
## 85 NA NA 0.000 0.000
## 86 NA NA 0.000 0.000
## 87 NA NA 4.396 4.396
## 88 NA NA 5.139 5.139
## 89 NA NA 6.976 6.976
## 90 NA NA 6.460 6.460
## 91 NA NA 2.644 2.644
## 92 NA NA 2.685 2.685
## 93 NA NA 6.238 6.238
## 94 NA NA 3.000 3.000
## 95 NA NA 5.855 5.855
## 96 NA NA NA NA
## 97 NA NA NA NA#unstandardized estimates
parameterEstimates(predict3)## lhs op rhs est se
## 1 Intercept =~ anxiety_T1 1.000 0.000
## 2 Intercept =~ anxiety_T2 1.000 0.000
## 3 Intercept =~ anxiety_T3 1.000 0.000
## 4 Intercept =~ anxiety_T4 1.000 0.000
## 5 Linear =~ anxiety_T1 0.000 0.000
## 6 Linear =~ anxiety_T2 1.000 0.000
## 7 Linear =~ anxiety_T3 2.000 0.000
## 8 Linear =~ anxiety_T4 3.000 0.000
## 9 Intercept ~ CASI_ADHD_CSum -0.023 0.015
## 10 Intercept ~ CASI_ADHD_CSum_T2 -0.020 0.014
## 11 Intercept ~ CASI_ADHD_CSum_T3 0.007 0.008
## 12 Intercept ~ CASI_ADHDCSum_T4 0.025 0.009
## 13 Intercept ~ Academicperformance_total_T4 0.042 0.024
## 14 Intercept ~ Familyincome_T1r -0.104 0.022
## 15 Intercept ~ Educationhousehold -0.023 0.048
## 16 Intercept ~ Child_gender -0.028 0.063
## 17 Intercept ~ Childagecategory -0.050 0.045
## 18 Linear ~ CASI_ADHD_CSum 0.009 0.007
## 19 Linear ~ CASI_ADHD_CSum_T2 0.028 0.007
## 20 Linear ~ CASI_ADHD_CSum_T3 0.005 0.005
## 21 Linear ~ CASI_ADHDCSum_T4 -0.019 0.007
## 22 Linear ~ Academicperformance_total_T4 -0.026 0.014
## 23 Linear ~ Familyincome_T1r 0.037 0.014
## 24 Linear ~ Educationhousehold -0.006 0.026
## 25 Linear ~ Child_gender 0.031 0.037
## 26 Linear ~ Childagecategory 0.007 0.023
## 27 anxiety_T1 ~ CASI_ADHD_CSum 0.089 0.022
## 28 anxiety_T2 ~ CASI_ADHD_CSum_T2 0.086 0.016
## 29 anxiety_T3 ~ CASI_ADHD_CSum_T3 0.050 0.014
## 30 anxiety_T4 ~ CASI_ADHDCSum_T4 0.056 0.022
## 31 anxiety_T1 ~~ anxiety_T1 0.118 0.035
## 32 anxiety_T2 ~~ anxiety_T2 0.047 0.012
## 33 anxiety_T3 ~~ anxiety_T3 0.058 0.010
## 34 anxiety_T4 ~~ anxiety_T4 0.103 0.023
## 35 Intercept ~~ Intercept -0.029 0.014
## 36 Linear ~~ Linear -0.009 0.004
## 37 Intercept ~~ Linear 0.015 0.007
## 38 CASI_ADHD_CSum ~~ CASI_ADHD_CSum 8.867 0.000
## 39 CASI_ADHD_CSum ~~ CASI_ADHD_CSum_T2 2.682 0.000
## 40 CASI_ADHD_CSum ~~ CASI_ADHD_CSum_T3 0.285 0.000
## 41 CASI_ADHD_CSum ~~ CASI_ADHDCSum_T4 -0.025 0.000
## 42 CASI_ADHD_CSum ~~ Academicperformance_total_T4 -0.573 0.000
## 43 CASI_ADHD_CSum ~~ Familyincome_T1r -0.941 0.000
## 44 CASI_ADHD_CSum ~~ Educationhousehold -0.585 0.000
## 45 CASI_ADHD_CSum ~~ Child_gender 0.420 0.000
## 46 CASI_ADHD_CSum ~~ Childagecategory 0.143 0.000
## 47 CASI_ADHD_CSum_T2 ~~ CASI_ADHD_CSum_T2 5.599 0.000
## 48 CASI_ADHD_CSum_T2 ~~ CASI_ADHD_CSum_T3 2.220 0.000
## 49 CASI_ADHD_CSum_T2 ~~ CASI_ADHDCSum_T4 3.092 0.000
## 50 CASI_ADHD_CSum_T2 ~~ Academicperformance_total_T4 0.265 0.000
## 51 CASI_ADHD_CSum_T2 ~~ Familyincome_T1r -0.640 0.000
## 52 CASI_ADHD_CSum_T2 ~~ Educationhousehold -0.571 0.000
## 53 CASI_ADHD_CSum_T2 ~~ Child_gender 0.098 0.000
## 54 CASI_ADHD_CSum_T2 ~~ Childagecategory 0.121 0.000
## 55 CASI_ADHD_CSum_T3 ~~ CASI_ADHD_CSum_T3 7.742 0.000
## 56 CASI_ADHD_CSum_T3 ~~ CASI_ADHDCSum_T4 1.855 0.000
## 57 CASI_ADHD_CSum_T3 ~~ Academicperformance_total_T4 0.046 0.000
## 58 CASI_ADHD_CSum_T3 ~~ Familyincome_T1r 0.191 0.000
## 59 CASI_ADHD_CSum_T3 ~~ Educationhousehold -0.477 0.000
## 60 CASI_ADHD_CSum_T3 ~~ Child_gender -0.330 0.000
## 61 CASI_ADHD_CSum_T3 ~~ Childagecategory 0.206 0.000
## 62 CASI_ADHDCSum_T4 ~~ CASI_ADHDCSum_T4 9.467 0.000
## 63 CASI_ADHDCSum_T4 ~~ Academicperformance_total_T4 0.431 0.000
## 64 CASI_ADHDCSum_T4 ~~ Familyincome_T1r -0.112 0.000
## 65 CASI_ADHDCSum_T4 ~~ Educationhousehold -0.484 0.000
## 66 CASI_ADHDCSum_T4 ~~ Child_gender -0.259 0.000
## 67 CASI_ADHDCSum_T4 ~~ Childagecategory 0.350 0.000
## 68 Academicperformance_total_T4 ~~ Academicperformance_total_T4 1.303 0.000
## 69 Academicperformance_total_T4 ~~ Familyincome_T1r 0.205 0.000
## 70 Academicperformance_total_T4 ~~ Educationhousehold -0.135 0.000
## 71 Academicperformance_total_T4 ~~ Child_gender -0.009 0.000
## 72 Academicperformance_total_T4 ~~ Childagecategory 0.004 0.000
## 73 Familyincome_T1r ~~ Familyincome_T1r 0.892 0.000
## 74 Familyincome_T1r ~~ Educationhousehold 0.168 0.000
## 75 Familyincome_T1r ~~ Child_gender -0.054 0.000
## 76 Familyincome_T1r ~~ Childagecategory -0.002 0.000
## 77 Educationhousehold ~~ Educationhousehold 0.533 0.000
## 78 Educationhousehold ~~ Child_gender 0.116 0.000
## 79 Educationhousehold ~~ Childagecategory 0.037 0.000
## 80 Child_gender ~~ Child_gender 0.250 0.000
## 81 Child_gender ~~ Childagecategory 0.009 0.000
## 82 Childagecategory ~~ Childagecategory 0.229 0.000
## 83 anxiety_T1 ~1 0.000 0.000
## 84 anxiety_T2 ~1 0.000 0.000
## 85 anxiety_T3 ~1 0.000 0.000
## 86 anxiety_T4 ~1 0.000 0.000
## 87 CASI_ADHD_CSum ~1 13.089 0.000
## 88 CASI_ADHD_CSum_T2 ~1 12.161 0.000
## 89 CASI_ADHD_CSum_T3 ~1 19.411 0.000
## 90 CASI_ADHDCSum_T4 ~1 19.875 0.000
## 91 Academicperformance_total_T4 ~1 3.018 0.000
## 92 Familyincome_T1r ~1 2.536 0.000
## 93 Educationhousehold ~1 4.554 0.000
## 94 Child_gender ~1 1.500 0.000
## 95 Childagecategory ~1 2.804 0.000
## 96 Intercept ~1 0.736 0.355
## 97 Linear ~1 -0.230 0.182
## z pvalue ci.lower ci.upper
## 1 NA NA 1.000 1.000
## 2 NA NA 1.000 1.000
## 3 NA NA 1.000 1.000
## 4 NA NA 1.000 1.000
## 5 NA NA 0.000 0.000
## 6 NA NA 1.000 1.000
## 7 NA NA 2.000 2.000
## 8 NA NA 3.000 3.000
## 9 -1.519 0.129 -0.054 0.007
## 10 -1.388 0.165 -0.048 0.008
## 11 0.902 0.367 -0.009 0.023
## 12 2.937 0.003 0.008 0.042
## 13 1.725 0.085 -0.006 0.089
## 14 -4.677 0.000 -0.147 -0.060
## 15 -0.488 0.625 -0.117 0.070
## 16 -0.442 0.658 -0.150 0.095
## 17 -1.101 0.271 -0.138 0.039
## 18 1.209 0.226 -0.005 0.022
## 19 3.766 0.000 0.013 0.042
## 20 0.986 0.324 -0.005 0.016
## 21 -2.719 0.007 -0.033 -0.005
## 22 -1.816 0.069 -0.054 0.002
## 23 2.715 0.007 0.010 0.064
## 24 -0.229 0.819 -0.056 0.045
## 25 0.851 0.395 -0.041 0.103
## 26 0.300 0.764 -0.038 0.052
## 27 4.042 0.000 0.046 0.132
## 28 5.281 0.000 0.054 0.118
## 29 3.477 0.001 0.022 0.079
## 30 2.580 0.010 0.013 0.099
## 31 3.345 0.001 0.049 0.186
## 32 3.880 0.000 0.023 0.071
## 33 5.536 0.000 0.037 0.078
## 34 4.408 0.000 0.057 0.148
## 35 -2.123 0.034 -0.056 -0.002
## 36 -2.130 0.033 -0.018 -0.001
## 37 2.102 0.036 0.001 0.029
## 38 NA NA 8.867 8.867
## 39 NA NA 2.682 2.682
## 40 NA NA 0.285 0.285
## 41 NA NA -0.025 -0.025
## 42 NA NA -0.573 -0.573
## 43 NA NA -0.941 -0.941
## 44 NA NA -0.585 -0.585
## 45 NA NA 0.420 0.420
## 46 NA NA 0.143 0.143
## 47 NA NA 5.599 5.599
## 48 NA NA 2.220 2.220
## 49 NA NA 3.092 3.092
## 50 NA NA 0.265 0.265
## 51 NA NA -0.640 -0.640
## 52 NA NA -0.571 -0.571
## 53 NA NA 0.098 0.098
## 54 NA NA 0.121 0.121
## 55 NA NA 7.742 7.742
## 56 NA NA 1.855 1.855
## 57 NA NA 0.046 0.046
## 58 NA NA 0.191 0.191
## 59 NA NA -0.477 -0.477
## 60 NA NA -0.330 -0.330
## 61 NA NA 0.206 0.206
## 62 NA NA 9.467 9.467
## 63 NA NA 0.431 0.431
## 64 NA NA -0.112 -0.112
## 65 NA NA -0.484 -0.484
## 66 NA NA -0.259 -0.259
## 67 NA NA 0.350 0.350
## 68 NA NA 1.303 1.303
## 69 NA NA 0.205 0.205
## 70 NA NA -0.135 -0.135
## 71 NA NA -0.009 -0.009
## 72 NA NA 0.004 0.004
## 73 NA NA 0.892 0.892
## 74 NA NA 0.168 0.168
## 75 NA NA -0.054 -0.054
## 76 NA NA -0.002 -0.002
## 77 NA NA 0.533 0.533
## 78 NA NA 0.116 0.116
## 79 NA NA 0.037 0.037
## 80 NA NA 0.250 0.250
## 81 NA NA 0.009 0.009
## 82 NA NA 0.229 0.229
## 83 NA NA 0.000 0.000
## 84 NA NA 0.000 0.000
## 85 NA NA 0.000 0.000
## 86 NA NA 0.000 0.000
## 87 NA NA 13.089 13.089
## 88 NA NA 12.161 12.161
## 89 NA NA 19.411 19.411
## 90 NA NA 19.875 19.875
## 91 NA NA 3.018 3.018
## 92 NA NA 2.536 2.536
## 93 NA NA 4.554 4.554
## 94 NA NA 1.500 1.500
## 95 NA NA 2.804 2.804
## 96 2.071 0.038 0.040 1.432
## 97 -1.266 0.205 -0.586 0.126# predicted means and covariances for observed variables
lavaan::fitted(predict3)## $cov
## anx_T1 anx_T2 anx_T3 anx_T4 CASI_ADHD_CSm
## anxiety_T1 0.145
## anxiety_T2 0.013 0.108
## anxiety_T3 0.015 0.039 0.103
## anxiety_T4 0.043 0.059 0.030 0.135
## CASI_ADHD_CSum 0.599 0.190 0.123 0.257 8.867
## CASI_ADHD_CSum_T2 0.242 0.593 0.333 0.503 2.682
## CASI_ADHD_CSum_T3 0.071 0.306 0.575 0.359 0.285
## CASI_ADHDCSum_T4 0.221 0.387 0.113 0.449 -0.025
## Academicperformance_total_T4 0.004 0.047 -0.004 -0.013 -0.573
## Familyincome_T1r -0.137 -0.106 -0.038 -0.052 -0.941
## Educationhousehold -0.083 -0.084 -0.063 -0.070 -0.585
## Child_gender 0.012 -0.002 -0.012 0.005 0.420
## Childagecategory 0.005 0.003 0.004 0.014 0.143
## CASI_ADHD_CS_T2 CASI_ADHD_CS_T3 CASI_ADHDC Ac__T4
## anxiety_T1
## anxiety_T2
## anxiety_T3
## anxiety_T4
## CASI_ADHD_CSum
## CASI_ADHD_CSum_T2 5.599
## CASI_ADHD_CSum_T3 2.220 7.742
## CASI_ADHDCSum_T4 3.092 1.855 9.467
## Academicperformance_total_T4 0.265 0.046 0.431 1.303
## Familyincome_T1r -0.640 0.191 -0.112 0.205
## Educationhousehold -0.571 -0.477 -0.484 -0.135
## Child_gender 0.098 -0.330 -0.259 -0.009
## Childagecategory 0.121 0.206 0.350 0.004
## Fml_T1 Edctnh Chld_g Chldgc
## anxiety_T1
## anxiety_T2
## anxiety_T3
## anxiety_T4
## CASI_ADHD_CSum
## CASI_ADHD_CSum_T2
## CASI_ADHD_CSum_T3
## CASI_ADHDCSum_T4
## Academicperformance_total_T4
## Familyincome_T1r 0.892
## Educationhousehold 0.168 0.533
## Child_gender -0.054 0.116 0.250
## Childagecategory -0.002 0.037 0.009 0.229
##
## $mean
## anxiety_T1 anxiety_T2
## 1.573 1.456
## anxiety_T3 anxiety_T4
## 1.393 1.532
## CASI_ADHD_CSum CASI_ADHD_CSum_T2
## 13.089 12.161
## CASI_ADHD_CSum_T3 CASI_ADHDCSum_T4
## 19.411 19.875
## Academicperformance_total_T4 Familyincome_T1r
## 3.018 2.536
## Educationhousehold Child_gender
## 4.554 1.500
## Childagecategory
## 2.804semPaths(predict3,what = "path", whatLabels = "est", edge.label.cex=.7,
intercepts = TRUE, edge.color = "black", nCharNodes = 0, nCharEdges=0,
sizeLat = 6, sizeMan=9, exoVar = FALSE, exoCov = FALSE,
shapeInt = "circle", covAtResiduals = FALSE)
# predicted means annd correlations for factors
lavaan::lavInspect(predict3, add.labels = TRUE, "mean.lv")## Intercept Linear
## 0.408 0.003lavaan::lavInspect(predict3, add.labels = TRUE, "cor.lv")## Intrcp Linear
## Intercept 1
## Linear NaN 1# residuals
lavaan::residuals(predict3, type = "raw")## $type
## [1] "raw"
##
## $cov
## anx_T1 anx_T2 anx_T3 anx_T4 CASI_ADHD_CSm
## anxiety_T1 -0.017
## anxiety_T2 0.008 -0.006
## anxiety_T3 0.002 -0.001 0.013
## anxiety_T4 -0.005 0.000 -0.005 -0.001
## CASI_ADHD_CSum -0.060 0.064 -0.069 0.036 0.000
## CASI_ADHD_CSum_T2 0.024 -0.008 -0.016 0.025 0.000
## CASI_ADHD_CSum_T3 0.115 -0.135 0.159 -0.091 0.000
## CASI_ADHDCSum_T4 -0.122 0.013 0.147 -0.185 0.000
## Academicperformance_total_T4 -0.031 0.004 0.037 -0.046 0.000
## Familyincome_T1r 0.017 -0.020 0.024 -0.014 0.000
## Educationhousehold -0.028 0.022 -0.011 -0.002 0.000
## Child_gender -0.016 0.026 -0.039 0.028 0.000
## Childagecategory -0.023 0.007 0.016 -0.024 0.000
## CASI_ADHD_CS_T2 CASI_ADHD_CS_T3 CASI_ADHDC Ac__T4
## anxiety_T1
## anxiety_T2
## anxiety_T3
## anxiety_T4
## CASI_ADHD_CSum
## CASI_ADHD_CSum_T2 0.000
## CASI_ADHD_CSum_T3 0.000 0.000
## CASI_ADHDCSum_T4 0.000 0.000 0.000
## Academicperformance_total_T4 0.000 0.000 0.000 0.000
## Familyincome_T1r 0.000 0.000 0.000 0.000
## Educationhousehold 0.000 0.000 0.000 0.000
## Child_gender 0.000 0.000 0.000 0.000
## Childagecategory 0.000 0.000 0.000 0.000
## Fml_T1 Edctnh Chld_g Chldgc
## anxiety_T1
## anxiety_T2
## anxiety_T3
## anxiety_T4
## CASI_ADHD_CSum
## CASI_ADHD_CSum_T2
## CASI_ADHD_CSum_T3
## CASI_ADHDCSum_T4
## Academicperformance_total_T4
## Familyincome_T1r 0.000
## Educationhousehold 0.000 0.000
## Child_gender 0.000 0.000 0.000
## Childagecategory 0.000 0.000 0.000 0.000
##
## $mean
## anxiety_T1 anxiety_T2
## 0.005 0.001
## anxiety_T3 anxiety_T4
## -0.008 0.009
## CASI_ADHD_CSum CASI_ADHD_CSum_T2
## 0.000 0.000
## CASI_ADHD_CSum_T3 CASI_ADHDCSum_T4
## 0.000 0.000
## Academicperformance_total_T4 Familyincome_T1r
## 0.000 0.000
## Educationhousehold Child_gender
## 0.000 0.000
## Childagecategory
## 0.000lavaan::residuals(predict3, type = "cor.bollen")## $type
## [1] "cor.bollen"
##
## $cov
## anx_T1 anx_T2 anx_T3 anx_T4 CASI_ADHD_CSm
## anxiety_T1 0.000
## anxiety_T2 0.079 0.000
## anxiety_T3 0.016 -0.024 0.000
## anxiety_T4 -0.015 0.011 -0.053 0.000
## CASI_ADHD_CSum -0.022 0.072 -0.075 0.033 0.000
## CASI_ADHD_CSum_T2 0.045 0.010 -0.045 0.030 0.000
## CASI_ADHD_CSum_T3 0.120 -0.142 0.131 -0.089 0.000
## CASI_ADHDCSum_T4 -0.099 0.023 0.134 -0.163 0.000
## Academicperformance_total_T4 -0.075 0.013 0.095 -0.111 0.000
## Familyincome_T1r 0.026 -0.076 0.083 -0.042 0.000
## Educationhousehold -0.126 0.083 -0.031 -0.008 0.000
## Child_gender -0.085 0.160 -0.226 0.153 0.000
## Childagecategory -0.131 0.046 0.098 -0.138 0.000
## CASI_ADHD_CS_T2 CASI_ADHD_CS_T3 CASI_ADHDC Ac__T4
## anxiety_T1
## anxiety_T2
## anxiety_T3
## anxiety_T4
## CASI_ADHD_CSum
## CASI_ADHD_CSum_T2 0.000
## CASI_ADHD_CSum_T3 0.000 0.000
## CASI_ADHDCSum_T4 0.000 0.000 0.000
## Academicperformance_total_T4 0.000 0.000 0.000 0.000
## Familyincome_T1r 0.000 0.000 0.000 0.000
## Educationhousehold 0.000 0.000 0.000 0.000
## Child_gender 0.000 0.000 0.000 0.000
## Childagecategory 0.000 0.000 0.000 0.000
## Fml_T1 Edctnh Chld_g Chldgc
## anxiety_T1
## anxiety_T2
## anxiety_T3
## anxiety_T4
## CASI_ADHD_CSum
## CASI_ADHD_CSum_T2
## CASI_ADHD_CSum_T3
## CASI_ADHDCSum_T4
## Academicperformance_total_T4
## Familyincome_T1r 0.000
## Educationhousehold 0.000 0.000
## Child_gender 0.000 0.000 0.000
## Childagecategory 0.000 0.000 0.000 0.000
##
## $mean
## anxiety_T1 anxiety_T2
## 0.013 0.002
## anxiety_T3 anxiety_T4
## -0.024 0.025
## CASI_ADHD_CSum CASI_ADHD_CSum_T2
## 0.000 0.000
## CASI_ADHD_CSum_T3 CASI_ADHDCSum_T4
## 0.000 0.000
## Academicperformance_total_T4 Familyincome_T1r
## 0.000 0.000
## Educationhousehold Child_gender
## 0.000 0.000
## Childagecategory
## 0.000#########################################################################################################
#########################################################################################################
# OLD - PROOF OF CONCEPT #
#########################################################################################################
cgm.dep = '
# intercept and slope with fixed coefficients
Intercept =~ 1*depression_T1 + 1*depression_T2 + 1*depression_T3 + 1*depression_T4
Slope =~ 0*depression_T1 + 1*depression_T2 + 2*depression_T3 + 3*depression_T4
Intercept ~ CASI_ADHD_CSum
Slope ~ CASI_ADHD_CSum
depression_T1 ~~ 0*depression_T1 #this is new
'
growth.fit <- lavaan::growth(cgm.dep, data=mydata, missing = "fiml", estimator = "mlr")
summary(growth.fit)## lavaan 0.6.17 ended normally after 29 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 10
##
## Used Total
## Number of observations 432 540
## Number of missing patterns 8
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 42.375 35.514
## Degrees of freedom 8 8
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.193
## Yuan-Bentler correction (Mplus variant)
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Intercept =~
## depression_T1 1.000
## depression_T2 1.000
## depression_T3 1.000
## depression_T4 1.000
## Slope =~
## depression_T1 0.000
## depression_T2 1.000
## depression_T3 2.000
## depression_T4 3.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## Intercept ~
## CASI_ADHD_CSum 0.098 0.008 12.015 0.000
## Slope ~
## CASI_ADHD_CSum -0.028 0.006 -4.697 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## .Intercept ~~
## .Slope -0.087 0.014 -6.426 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .Intercept 0.103 0.089 1.167 0.243
## .Slope 0.321 0.079 4.068 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .depression_T1 0.000
## .depression_T2 0.240 0.039 6.163 0.000
## .depression_T3 0.243 0.034 7.213 0.000
## .depression_T4 0.174 0.052 3.353 0.001
## .Intercept 0.321 0.027 11.966 0.000
## .Slope 0.039 0.007 5.392 0.000fitMeasures(growth.fit)## npar fmin
## 10.000 0.049
## chisq df
## 42.375 8.000
## pvalue chisq.scaled
## 0.000 35.514
## df.scaled pvalue.scaled
## 8.000 0.000
## chisq.scaling.factor baseline.chisq
## 1.193 221.110
## baseline.df baseline.pvalue
## 10.000 0.000
## baseline.chisq.scaled baseline.df.scaled
## 170.520 10.000
## baseline.pvalue.scaled baseline.chisq.scaling.factor
## 0.000 1.297
## cfi tli
## 0.837 0.796
## cfi.scaled tli.scaled
## 0.829 0.786
## cfi.robust tli.robust
## 0.722 0.652
## nnfi rfi
## 0.796 0.760
## nfi pnfi
## 0.808 0.647
## ifi rni
## 0.839 0.837
## nnfi.scaled rfi.scaled
## 0.786 0.740
## nfi.scaled pnfi.scaled
## 0.792 0.633
## ifi.scaled rni.scaled
## 0.831 0.829
## nnfi.robust rni.robust
## 0.652 0.722
## logl unrestricted.logl
## -586.631 -565.443
## aic bic
## 1193.261 1233.945
## ntotal bic2
## 432.000 1202.211
## scaling.factor.h1 scaling.factor.h0
## 1.163 1.138
## rmsea rmsea.ci.lower
## 0.100 0.071
## rmsea.ci.upper rmsea.ci.level
## 0.130 0.900
## rmsea.pvalue rmsea.close.h0
## 0.003 0.050
## rmsea.notclose.pvalue rmsea.notclose.h0
## 0.880 0.080
## rmsea.scaled rmsea.ci.lower.scaled
## 0.089 0.063
## rmsea.ci.upper.scaled rmsea.pvalue.scaled
## 0.117 0.009
## rmsea.notclose.pvalue.scaled rmsea.robust
## 0.737 0.210
## rmsea.ci.lower.robust rmsea.ci.upper.robust
## 0.123 0.303
## rmsea.pvalue.robust rmsea.notclose.pvalue.robust
## 0.003 0.990
## rmr rmr_nomean
## 0.134 0.154
## srmr srmr_bentler
## 0.246 0.246
## srmr_bentler_nomean crmr
## 0.283 0.113
## crmr_nomean srmr_mplus
## 0.136 0.258
## srmr_mplus_nomean cn_05
## 0.251 159.091
## cn_01 gfi
## 205.813 0.969
## agfi pgfi
## 0.923 0.388
## mfi ecvi
## 0.961 0.144graph_sem(model = growth.fit)
parameterEstimates(growth.fit)## lhs op rhs est se z pvalue ci.lower
## 1 Intercept =~ depression_T1 1.000 0.000 NA NA 1.000
## 2 Intercept =~ depression_T2 1.000 0.000 NA NA 1.000
## 3 Intercept =~ depression_T3 1.000 0.000 NA NA 1.000
## 4 Intercept =~ depression_T4 1.000 0.000 NA NA 1.000
## 5 Slope =~ depression_T1 0.000 0.000 NA NA 0.000
## 6 Slope =~ depression_T2 1.000 0.000 NA NA 1.000
## 7 Slope =~ depression_T3 2.000 0.000 NA NA 2.000
## 8 Slope =~ depression_T4 3.000 0.000 NA NA 3.000
## 9 Intercept ~ CASI_ADHD_CSum 0.098 0.008 12.015 0.000 0.082
## 10 Slope ~ CASI_ADHD_CSum -0.028 0.006 -4.697 0.000 -0.039
## 11 depression_T1 ~~ depression_T1 0.000 0.000 NA NA 0.000
## 12 depression_T2 ~~ depression_T2 0.240 0.039 6.163 0.000 0.164
## 13 depression_T3 ~~ depression_T3 0.243 0.034 7.213 0.000 0.177
## 14 depression_T4 ~~ depression_T4 0.174 0.052 3.353 0.001 0.072
## 15 Intercept ~~ Intercept 0.321 0.027 11.966 0.000 0.269
## 16 Slope ~~ Slope 0.039 0.007 5.392 0.000 0.025
## 17 Intercept ~~ Slope -0.087 0.014 -6.426 0.000 -0.114
## 18 CASI_ADHD_CSum ~~ CASI_ADHD_CSum 16.373 0.000 NA NA 16.373
## 19 depression_T1 ~1 0.000 0.000 NA NA 0.000
## 20 depression_T2 ~1 0.000 0.000 NA NA 0.000
## 21 depression_T3 ~1 0.000 0.000 NA NA 0.000
## 22 depression_T4 ~1 0.000 0.000 NA NA 0.000
## 23 CASI_ADHD_CSum ~1 11.106 0.000 NA NA 11.106
## 24 Intercept ~1 0.103 0.089 1.167 0.243 -0.070
## 25 Slope ~1 0.321 0.079 4.068 0.000 0.166
## ci.upper
## 1 1.000
## 2 1.000
## 3 1.000
## 4 1.000
## 5 0.000
## 6 1.000
## 7 2.000
## 8 3.000
## 9 0.114
## 10 -0.016
## 11 0.000
## 12 0.316
## 13 0.309
## 14 0.276
## 15 0.374
## 16 0.054
## 17 -0.061
## 18 16.373
## 19 0.000
## 20 0.000
## 21 0.000
## 22 0.000
## 23 11.106
## 24 0.277
## 25 0.476standardizedSolution(growth.fit, type = "std.all", se= TRUE, pvalue = TRUE)## lhs op rhs est.std se z pvalue ci.lower
## 1 Intercept =~ depression_T1 1.000 0.000 NA NA 1.000
## 2 Intercept =~ depression_T2 0.971 0.048 20.342 0.00 0.878
## 3 Intercept =~ depression_T3 1.090 0.067 16.260 0.00 0.958
## 4 Intercept =~ depression_T4 1.202 0.081 14.814 0.00 1.043
## 5 Slope =~ depression_T1 0.000 0.000 NA NA 0.000
## 6 Slope =~ depression_T2 0.321 0.040 8.067 0.00 0.243
## 7 Slope =~ depression_T3 0.720 0.097 7.436 0.00 0.531
## 8 Slope =~ depression_T4 1.192 0.131 9.078 0.00 0.935
## 9 Intercept ~ CASI_ADHD_CSum 0.572 0.043 13.327 0.00 0.488
## 10 Slope ~ CASI_ADHD_CSum -0.493 0.081 -6.082 0.00 -0.652
## 11 depression_T1 ~~ depression_T1 0.000 0.000 NA NA 0.000
## 12 depression_T2 ~~ depression_T2 0.474 0.047 10.172 0.00 0.383
## 13 depression_T3 ~~ depression_T3 0.605 0.081 7.503 0.00 0.447
## 14 depression_T4 ~~ depression_T4 0.528 0.151 3.486 0.00 0.231
## 15 Intercept ~~ Intercept 0.673 0.049 13.725 0.00 0.577
## 16 Slope ~~ Slope 0.757 0.080 9.448 0.00 0.600
## 17 Intercept ~~ Slope -0.775 0.079 -9.811 0.00 -0.930
## 18 CASI_ADHD_CSum ~~ CASI_ADHD_CSum 1.000 0.000 NA NA 1.000
## 19 depression_T1 ~1 0.000 0.000 NA NA 0.000
## 20 depression_T2 ~1 0.000 0.000 NA NA 0.000
## 21 depression_T3 ~1 0.000 0.000 NA NA 0.000
## 22 depression_T4 ~1 0.000 0.000 NA NA 0.000
## 23 CASI_ADHD_CSum ~1 2.745 0.000 NA NA 2.745
## 24 Intercept ~1 0.150 0.130 1.150 0.25 -0.105
## 25 Slope ~1 1.407 0.279 5.036 0.00 0.859
## ci.upper
## 1 1.000
## 2 1.065
## 3 1.221
## 4 1.361
## 5 0.000
## 6 0.399
## 7 0.910
## 8 1.450
## 9 0.656
## 10 -0.334
## 11 0.000
## 12 0.566
## 13 0.763
## 14 0.825
## 15 0.769
## 16 0.913
## 17 -0.621
## 18 1.000
## 19 0.000
## 20 0.000
## 21 0.000
## 22 0.000
## 23 2.745
## 24 0.405
## 25 1.954#########################################################################################################
#########################################################################################################
#########################################################################################################
#########################################################################################################
#########################################################################################################
# OLD - PROOF OF CONCEPT #
#relaxing constrains to allow for full info from TVCs
#########################################################################################################
#sample model code
# model <- '
# # intercept and slope with fixed coefficients
# i =~ 1*t1 + 1*t2 + 1*t3 + 1*t4
# s =~ 0*t1 + 1*t2 + 2*t3 + 3*t4
# # regressions that affect the latent growth factors
# i ~ x1 + x2
# s ~ x1 + x2
# # time-varying covariates
# t1 ~ c1
# t2 ~ c2
# t3 ~ c3
# t4 ~ c4
# '
cgm.dep = '
# intercept and slope with fixed coefficients
Intercept =~ 1*depression_T1 + 1*depression_T2 + 1*depression_T3 + 1*depression_T4
Slope =~ 0*depression_T1 + 1*depression_T2 + 2*depression_T3 + 3*depression_T4
# regressions of covariates that affect the latent growth factors
Intercept ~ CASI_ADHD_CSum
Slope ~ CASI_ADHD_CSum
# time-varying covariates
anxiety_T1 ~ CASI_ADHD_CSum
anxiety_T2 ~ CASI_ADHD_CSum_T2
anxiety_T3 ~ CASI_ADHD_CSum_T3
anxiety_T4 ~ CASI_ADHDCSum_T4'
growth.fit <- lavaan::growth(cgm.dep, data=mydata, missing = "fiml", estimator = "mlr")
summary(growth.fit)## lavaan 0.6.17 ended normally after 138 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 33
##
## Used Total
## Number of observations 57 540
## Number of missing patterns 1
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 180.018 197.573
## Degrees of freedom 43 43
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.911
## Yuan-Bentler correction (Mplus variant)
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Intercept =~
## depression_T1 1.000
## depression_T2 1.000
## depression_T3 1.000
## depression_T4 1.000
## Slope =~
## depression_T1 0.000
## depression_T2 1.000
## depression_T3 2.000
## depression_T4 3.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## Intercept ~
## CASI_ADHD_CSum 0.089 0.013 6.861 0.000
## Slope ~
## CASI_ADHD_CSum -0.025 0.011 -2.211 0.027
## anxiety_T1 ~
## CASI_ADHD_CSum 0.117 0.004 30.885 0.000
## anxiety_T2 ~
## CASI_ADHD_CS_T 0.116 0.004 32.182 0.000
## anxiety_T3 ~
## CASI_ADHD_CS_T 0.071 0.002 45.729 0.000
## anxiety_T4 ~
## CASI_ADHDCS_T4 0.075 0.003 29.936 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## .Intercept ~~
## .Slope 0.006 0.017 0.334 0.739
## .anxiety_T1 0.050 0.019 2.704 0.007
## .anxiety_T2 0.018 0.026 0.708 0.479
## .anxiety_T3 0.027 0.012 2.239 0.025
## .anxiety_T4 0.027 0.017 1.601 0.109
## .Slope ~~
## .anxiety_T1 -0.011 0.009 -1.182 0.237
## .anxiety_T2 -0.002 0.009 -0.237 0.812
## .anxiety_T3 0.000 0.006 0.079 0.937
## .anxiety_T4 0.001 0.009 0.153 0.878
## .anxiety_T1 ~~
## .anxiety_T2 0.003 0.011 0.287 0.774
## .anxiety_T3 0.001 0.009 0.160 0.873
## .anxiety_T4 -0.000 0.018 -0.024 0.981
## .anxiety_T2 ~~
## .anxiety_T3 0.013 0.008 1.692 0.091
## .anxiety_T4 0.003 0.013 0.250 0.802
## .anxiety_T3 ~~
## .anxiety_T4 -0.001 0.011 -0.129 0.897
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .Intercept 0.280 0.179 1.561 0.118
## .Slope 0.309 0.157 1.972 0.049
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .depression_T1 0.171 0.059 2.888 0.004
## .depression_T2 0.140 0.027 5.260 0.000
## .depression_T3 0.151 0.038 3.922 0.000
## .depression_T4 0.135 0.049 2.740 0.006
## .anxiety_T1 0.124 0.031 4.019 0.000
## .anxiety_T2 0.062 0.020 3.157 0.002
## .anxiety_T3 0.052 0.010 5.022 0.000
## .anxiety_T4 0.150 0.026 5.803 0.000
## .Intercept 0.006 0.043 0.131 0.896
## .Slope 0.004 0.008 0.501 0.617fitMeasures(growth.fit)## npar fmin
## 33.000 1.579
## chisq df
## 180.018 43.000
## pvalue chisq.scaled
## 0.000 197.573
## df.scaled pvalue.scaled
## 43.000 0.000
## chisq.scaling.factor baseline.chisq
## 0.911 323.866
## baseline.df baseline.pvalue
## 60.000 0.000
## baseline.chisq.scaled baseline.df.scaled
## 351.638 60.000
## baseline.pvalue.scaled baseline.chisq.scaling.factor
## 0.000 0.921
## cfi tli
## 0.481 0.275
## cfi.scaled tli.scaled
## 0.470 0.260
## cfi.robust tli.robust
## 0.488 0.286
## nnfi rfi
## 0.275 0.224
## nfi pnfi
## 0.444 0.318
## ifi rni
## 0.512 0.481
## nnfi.scaled rfi.scaled
## 0.260 0.216
## nfi.scaled pnfi.scaled
## 0.438 0.314
## ifi.scaled rni.scaled
## 0.499 0.470
## nnfi.robust rni.robust
## 0.286 0.488
## logl unrestricted.logl
## -150.674 -60.665
## aic bic
## 367.348 434.769
## ntotal bic2
## 57.000 331.031
## scaling.factor.h1 scaling.factor.h0
## 1.029 1.182
## rmsea rmsea.ci.lower
## 0.236 0.201
## rmsea.ci.upper rmsea.ci.level
## 0.273 0.900
## rmsea.pvalue rmsea.close.h0
## 0.000 0.050
## rmsea.notclose.pvalue rmsea.notclose.h0
## 1.000 0.080
## rmsea.scaled rmsea.ci.lower.scaled
## 0.251 0.215
## rmsea.ci.upper.scaled rmsea.pvalue.scaled
## 0.289 0.000
## rmsea.notclose.pvalue.scaled rmsea.robust
## 1.000 0.234
## rmsea.ci.lower.robust rmsea.ci.upper.robust
## 0.197 0.271
## rmsea.pvalue.robust rmsea.notclose.pvalue.robust
## 0.000 1.000
## rmr rmr_nomean
## 0.175 0.187
## srmr srmr_bentler
## 0.233 0.233
## srmr_bentler_nomean crmr
## 0.249 0.183
## crmr_nomean srmr_mplus
## 0.197 0.302
## srmr_mplus_nomean cn_05
## 0.233 19.778
## cn_01 gfi
## 22.360 0.977
## agfi pgfi
## 0.952 0.467
## mfi ecvi
## 0.301 4.316graph_sem(model = growth.fit)
parameterEstimates(growth.fit)## lhs op rhs est se z pvalue ci.lower
## 1 Intercept =~ depression_T1 1.000 0.000 NA NA 1.000
## 2 Intercept =~ depression_T2 1.000 0.000 NA NA 1.000
## 3 Intercept =~ depression_T3 1.000 0.000 NA NA 1.000
## 4 Intercept =~ depression_T4 1.000 0.000 NA NA 1.000
## 5 Slope =~ depression_T1 0.000 0.000 NA NA 0.000
## 6 Slope =~ depression_T2 1.000 0.000 NA NA 1.000
## 7 Slope =~ depression_T3 2.000 0.000 NA NA 2.000
## 8 Slope =~ depression_T4 3.000 0.000 NA NA 3.000
## 9 Intercept ~ CASI_ADHD_CSum 0.089 0.013 6.861 0.000 0.064
## 10 Slope ~ CASI_ADHD_CSum -0.025 0.011 -2.211 0.027 -0.047
## 11 anxiety_T1 ~ CASI_ADHD_CSum 0.117 0.004 30.885 0.000 0.110
## 12 anxiety_T2 ~ CASI_ADHD_CSum_T2 0.116 0.004 32.182 0.000 0.109
## 13 anxiety_T3 ~ CASI_ADHD_CSum_T3 0.071 0.002 45.729 0.000 0.068
## 14 anxiety_T4 ~ CASI_ADHDCSum_T4 0.075 0.003 29.936 0.000 0.071
## 15 depression_T1 ~~ depression_T1 0.171 0.059 2.888 0.004 0.055
## 16 depression_T2 ~~ depression_T2 0.140 0.027 5.260 0.000 0.088
## 17 depression_T3 ~~ depression_T3 0.151 0.038 3.922 0.000 0.075
## 18 depression_T4 ~~ depression_T4 0.135 0.049 2.740 0.006 0.038
## 19 anxiety_T1 ~~ anxiety_T1 0.124 0.031 4.019 0.000 0.063
## 20 anxiety_T2 ~~ anxiety_T2 0.062 0.020 3.157 0.002 0.023
## 21 anxiety_T3 ~~ anxiety_T3 0.052 0.010 5.022 0.000 0.032
## 22 anxiety_T4 ~~ anxiety_T4 0.150 0.026 5.803 0.000 0.100
## 23 Intercept ~~ Intercept 0.006 0.043 0.131 0.896 -0.078
## 24 Slope ~~ Slope 0.004 0.008 0.501 0.617 -0.011
## 25 Intercept ~~ Slope 0.006 0.017 0.334 0.739 -0.028
## 26 Intercept ~~ anxiety_T1 0.050 0.019 2.704 0.007 0.014
## 27 Intercept ~~ anxiety_T2 0.018 0.026 0.708 0.479 -0.032
## 28 Intercept ~~ anxiety_T3 0.027 0.012 2.239 0.025 0.003
## 29 Intercept ~~ anxiety_T4 0.027 0.017 1.601 0.109 -0.006
## 30 Slope ~~ anxiety_T1 -0.011 0.009 -1.182 0.237 -0.029
## 31 Slope ~~ anxiety_T2 -0.002 0.009 -0.237 0.812 -0.020
## 32 Slope ~~ anxiety_T3 0.000 0.006 0.079 0.937 -0.011
## 33 Slope ~~ anxiety_T4 0.001 0.009 0.153 0.878 -0.017
## 34 anxiety_T1 ~~ anxiety_T2 0.003 0.011 0.287 0.774 -0.018
## 35 anxiety_T1 ~~ anxiety_T3 0.001 0.009 0.160 0.873 -0.016
## 36 anxiety_T1 ~~ anxiety_T4 0.000 0.018 -0.024 0.981 -0.036
## 37 anxiety_T2 ~~ anxiety_T3 0.013 0.008 1.692 0.091 -0.002
## 38 anxiety_T2 ~~ anxiety_T4 0.003 0.013 0.250 0.802 -0.022
## 39 anxiety_T3 ~~ anxiety_T4 -0.001 0.011 -0.129 0.897 -0.023
## 40 CASI_ADHD_CSum ~~ CASI_ADHD_CSum 8.857 0.000 NA NA 8.857
## 41 CASI_ADHD_CSum ~~ CASI_ADHD_CSum_T2 3.229 0.000 NA NA 3.229
## 42 CASI_ADHD_CSum ~~ CASI_ADHD_CSum_T3 0.861 0.000 NA NA 0.861
## 43 CASI_ADHD_CSum ~~ CASI_ADHDCSum_T4 0.133 0.000 NA NA 0.133
## 44 CASI_ADHD_CSum_T2 ~~ CASI_ADHD_CSum_T2 7.917 0.000 NA NA 7.917
## 45 CASI_ADHD_CSum_T2 ~~ CASI_ADHD_CSum_T3 4.546 0.000 NA NA 4.546
## 46 CASI_ADHD_CSum_T2 ~~ CASI_ADHDCSum_T4 3.675 0.000 NA NA 3.675
## 47 CASI_ADHD_CSum_T3 ~~ CASI_ADHD_CSum_T3 9.921 0.000 NA NA 9.921
## 48 CASI_ADHD_CSum_T3 ~~ CASI_ADHDCSum_T4 2.447 0.000 NA NA 2.447
## 49 CASI_ADHDCSum_T4 ~~ CASI_ADHDCSum_T4 9.469 0.000 NA NA 9.469
## 50 depression_T1 ~1 0.000 0.000 NA NA 0.000
## 51 depression_T2 ~1 0.000 0.000 NA NA 0.000
## 52 depression_T3 ~1 0.000 0.000 NA NA 0.000
## 53 depression_T4 ~1 0.000 0.000 NA NA 0.000
## 54 anxiety_T1 ~1 0.000 0.000 NA NA 0.000
## 55 anxiety_T2 ~1 0.000 0.000 NA NA 0.000
## 56 anxiety_T3 ~1 0.000 0.000 NA NA 0.000
## 57 anxiety_T4 ~1 0.000 0.000 NA NA 0.000
## 58 CASI_ADHD_CSum ~1 13.140 0.000 NA NA 13.140
## 59 CASI_ADHD_CSum_T2 ~1 12.368 0.000 NA NA 12.368
## 60 CASI_ADHD_CSum_T3 ~1 19.614 0.000 NA NA 19.614
## 61 CASI_ADHDCSum_T4 ~1 19.930 0.000 NA NA 19.930
## 62 Intercept ~1 0.280 0.179 1.561 0.118 -0.072
## 63 Slope ~1 0.309 0.157 1.972 0.049 0.002
## ci.upper
## 1 1.000
## 2 1.000
## 3 1.000
## 4 1.000
## 5 0.000
## 6 1.000
## 7 2.000
## 8 3.000
## 9 0.115
## 10 -0.003
## 11 0.125
## 12 0.123
## 13 0.074
## 14 0.080
## 15 0.287
## 16 0.193
## 17 0.226
## 18 0.231
## 19 0.184
## 20 0.100
## 21 0.072
## 22 0.201
## 23 0.089
## 24 0.019
## 25 0.039
## 26 0.086
## 27 0.069
## 28 0.051
## 29 0.060
## 30 0.007
## 31 0.016
## 32 0.012
## 33 0.020
## 34 0.024
## 35 0.018
## 36 0.035
## 37 0.028
## 38 0.029
## 39 0.020
## 40 8.857
## 41 3.229
## 42 0.861
## 43 0.133
## 44 7.917
## 45 4.546
## 46 3.675
## 47 9.921
## 48 2.447
## 49 9.469
## 50 0.000
## 51 0.000
## 52 0.000
## 53 0.000
## 54 0.000
## 55 0.000
## 56 0.000
## 57 0.000
## 58 13.140
## 59 12.368
## 60 19.614
## 61 19.930
## 62 0.632
## 63 0.617#########################################################################################################